Method and Apparatus for Vector Based Finite Impulse Response (FIR) Filtering

ABSTRACT

A method is provided that includes performing, by a processor in response to a vector finite impulse response (VFIR) filter instruction, generating of a plurality of filter outputs using a plurality of coefficients and a plurality of sequential data elements, the plurality of coefficients specified by a coefficient operand of the VFIR filter instruction and the plurality of sequential data elements specified by a data operand of the VFIR filter instruction, and storing the filter outputs in a storage location specified by the VFIR filter instruction.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of U.S. Provisional Patent ApplicationNo. 62/852,870 filed May 24, 2019, which is incorporated herein byreference in its entirety.

BACKGROUND

Digital signal processors (DSP) are optimized for processing streams ofdata that may be derived from various input signals, such as sensordata, a video stream, a voice channel, radar signals, biomedicalsignals, etc. Digital signal processors operating on real-time datatypically receive an input data stream, perform a filter function on thedata stream (such as encoding or decoding) and output a transformed datastream. The system is called real-time because the application fails ifthe transformed data stream is not available for output when scheduled.Typical video encoding requires a predictable but non-sequential inputdata pattern. A typical application requires memory access to load dataregisters in a data register file and then supply data from the dataregisters to functional units which perform the data processing.

One or more DSP processing cores can be combined with various peripheralcircuits, blocks of memory, etc. on a single integrated circuit (IC) dieto form a system on chip (SoC). These systems can include multipleinterconnected processors that share the use of on-chip and off-chipmemory. A processor can include some combination of instruction cache(ICache) and data cache (DCache) to improve processing. Furthermore,multiple processors with shared memory can be incorporated in a singleembedded system. The processors can physically share the same memorywithout accessing data or executing code located in the same memorylocations or can use some portion of the shared memory as common sharedmemory.

SUMMARY

Embodiments of the present disclosure relate to methods and apparatusfor vector based finite impulse response (FIR) filtering. In one aspect,a method is provided that includes performing, by a processor inresponse to a vector finite impulse response (VFIR) filter instruction,generating of a plurality of filter outputs using a plurality ofcoefficients and a plurality of sequential data elements, the pluralityof coefficients specified by a coefficient operand of the VFIR filterinstruction and the plurality of sequential data elements specified by adata operand of the VFIR filter instruction, and storing the filteroutputs in a storage location specified by the VFIR filter instruction.

In one aspect, a processor is provided that includes an instructiondecoder configured to decode a vector finite impulse response (VFIR)filter instruction, and filter computation logic configured to generate,responsive to the VFIR filter instruction, a plurality of filter outputsusing a plurality of coefficients and a plurality of sequential dataelements, the plurality of coefficients specified by a coefficientoperand of the VFIR filter instruction and the plurality of sequentialdata elements specified by a data operand of the VFIR filterinstruction.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example dual scalar/vector data path processor;

FIG. 2 illustrates the registers and functional units in the dualscalar/vector data path processor illustrated in FIG. 1;

FIG. 3 illustrates a global scalar register file;

FIG. 4 illustrates a local scalar register file shared by arithmeticfunctional units;

FIG. 5 illustrates a local scalar register file shared by multiplyfunctional units;

FIG. 6 illustrates a local scalar register file shared by load/storeunits;

FIG. 7 illustrates a global vector register file;

FIG. 8 illustrates a predicate register file;

FIG. 9 illustrates a local vector register file shared by arithmeticfunctional units;

FIG. 10 illustrates a local vector register file shared by multiply andcorrelation functional units;

FIG. 11 illustrates pipeline phases of a processing unit;

FIG. 12 illustrates sixteen instructions of a single fetch packet;

FIG. 13 illustrates an example of instruction coding;

FIG. 14 illustrates bit coding of a condition code extension slot 0;

FIG. 15 illustrates bit coding of a condition code extension slot 1;

FIG. 16 illustrates bit coding of a constant extension slot 0;

FIG. 17 is a partial block diagram illustrating constant extension;

FIG. 18 illustrates carry control for SIMD operations;

FIG. 19 illustrates a conceptual view of streaming engines;

FIG. 20 illustrates a sequence of formatting operations;

FIG. 21 illustrates an example of lane allocation in a vector;

FIG. 22 illustrates an example of lane allocation in a vector;

FIG. 23 illustrates a basic two-dimensional (2D) stream;

FIG. 24 illustrates the order of elements within the example stream ofFIG. 23;

FIG. 25 illustrates extracting a smaller rectangle from a largerrectangle;

FIG. 26 illustrates how an example streaming engine fetches a streamwith a transposition granularity of 4 bytes;

FIG. 27 illustrates how an example streaming engine fetches a streamwith a transposition granularity of 8 bytes;

FIG. 28 illustrates the details of an example streaming engine;

FIG. 29 illustrates an example stream template register;

FIG. 30 illustrates sub-field definitions of the flags field of theexample stream template register of FIG. 29;

FIG. 31 illustrates an example of a vector length masking/groupduplication block;

FIG. 32 is a partial schematic diagram of an example of the generationof the streaming engine valid or invalid indication;

FIG. 33 is a partial schematic diagram of a streaming engine addressgenerator illustrating generation of the loop address and loop count;

FIG. 34 illustrates a partial schematic diagram showing the streamingengine supply of data of this example;

FIG. 35 illustrates a partial schematic diagram showing the streamingengine supply of valid data to the predicate unit;

FIG. 36 illustrates an example of M unit slice multiply logic and N unitslice multiply logic in a data path;

FIG. 37 illustrates an instruction format for a dual issue instruction;

FIG. 38 illustrates a block diagram of instruction decoding for a dualissue instruction;

FIG. 39 illustrates an example of M unit slice multiply logic and N unitslice multiply logic in which the M unit slice multiply logic includesboth arithmetic logic and multiply logic;

FIG. 40 is a flow diagram of a method for performing multiplyinstructions;

FIG. 41 is a flow diagram of a method for performing a dual issuemultiply instruction;

FIGS. 42 and 43 are flow diagrams of methods for performing a floatingpoint multiply instruction;

FIG. 44 illustrates an example of the operation of VFIR8×HDinstructions;

FIG. 45 illustrates an example of multiplier use for the VFIR8×HDinstructions;

FIG. 46 illustrates an example of the operation of VFIR8×HWinstructions;

FIG. 47 illustrates an example of multiplier use for the VFIR8×HWinstructions;

FIG. 48 illustrates an example of the operation of VFIR4×HWinstructions;

FIG. 49 illustrates an example of multiplier use for the VFIR4×HWinstructions;

FIG. 50 is a flow diagram of a method for performing a vector finiteimpulse response (VFIR) filer instruction;

FIG. 51 illustrates an example of the operation of VMATMPY×HWinstructions;

FIG. 52 illustrates mapping of matrix elements to slices for VMATMPY×HWinstructions;

FIG. 53 illustrates an example of multiplier use for the VMATMPY×HWinstructions;

FIG. 54 illustrates an example of the operation of VMATMPY×HDinstructions;

FIG. 55 illustrates mapping of matrix elements to slices for VMATMPY×HDinstructions;

FIG. 56 illustrates an example of multiplier use for the VMATMPY×HDinstructions;

FIG. 57 is a flow diagram of a method for performing vector-based matrixmultiplication;

FIG. 58 illustrates a block diagram of connectivity between thestreaming engine and the vector multiplication units;

FIG. 59 is a block diagram illustrating an example streaming engineinterface;

FIG. 60 is a flow diagram of a method for performing permutation ofstreamed data elements; and

FIG. 61 is a block diagram of a multiprocessor system.

DETAILED DESCRIPTION

Like elements in the various figures are denoted by like referencenumerals for consistency.

Digital signal processors (DSP) are optimized for processing streams ofdata that can be derived from various input signals, such as sensordata, a video stream, a voice channel, radar signals, biomedicalsignals, etc. Memory bandwidth and scheduling are concerns for digitalsignal processors operating on real-time data. An example DSP processingcore is described herein that includes a streaming engine to improvememory bandwidth and data scheduling.

One or more DSP processing cores can be combined with various peripheralcircuits, blocks of memory, etc. on a single integrated circuit (IC) dieto form a system on chip (SoC). See, for example, “66AK2Hx MulticoreKeystone™ DSP+ARM® System-on-Chips,” 2013 which is incorporated byreference herein.

In the example DSP core described herein, an autonomous streaming engine(SE) is coupled to the DSP. In this example, the streaming engineincludes two closely coupled streaming engines that can manage two datastreams simultaneously. In another example, the streaming engine iscapable of managing only a single stream, while in other examples thestreaming engine is capable of handling more than two streams. In eachcase, for each stream, the streaming engine includes an addressgeneration stage, a data formatting stage, and some storage forformatted data waiting for consumption by the processor. In the examplesdescribed herein, addresses are derived from algorithms that can involvemulti-dimensional loops, each dimension maintaining an iteration count.In one example, the streaming engine supports six levels of nestediteration. In other examples, more or fewer levels of iteration aresupported.

Further, in the example DSP core described herein, instruction supportis provided for various operations that are important for computervision processing and other applications. In various examples, supportis provided for one or more of vector based finite impulse filtering(FIR), vector and scalar multiplication, vector and scalar floatingpoint multiplication, and vector based matrix multiplication.

FIG. 1 illustrates an example processor 100 that includes dualscalar/vector data paths 115, 117. Processor 100 includes a streamingengine 125 that is described in more detail herein. Processor 100includes separate level one instruction cache (L1I) 121 and level onedata cache (L1D) 123. Processor 100 includes a level 2 (L2) combinedinstruction/data cache 130 that holds both instructions and data. FIG. 1illustrates connection between L1I cache and L2 combinedinstruction/data cache 130, 512-bit bus 142. FIG. 1 illustrates theconnection between L1D cache 123 and L2 combined instruction/data cache130, 512-bit bus 145. In the example processor 100, L2 combinedinstruction/data cache 130 stores both instructions to back up L1I cache121 and data to back up L1D cache 123. In this example, L2 combinedinstruction/data cache 130 is further connected to higher level cacheand/or main memory using known or later developed memory systemtechniques not illustrated in FIG. 1. As used herein, the term “higherlevel” memory or cache refers to a next level in a memory hierarchy thatis more distant from the processor, while the term “lower level” memoryor cache refers to a level in the memory hierarchy that is closer to theprocessor. L1I cache 121, L1D cache 123, and L2 cache 130 may beimplemented in different sizes in various examples. In this example, L1Icache 121 and L1D cache 123 are each 32K bytes, and L2 cache 130 is1024K bytes. In the example processor 100, L1I cache 121, L1D cache 123and L2 combined instruction/data cache 130 are formed on a singleintegrated circuit. This single integrated circuit optionally includesother circuits.

Processing unit core 110 fetches instructions from L1I cache 121 ascontrolled by instruction fetch unit 111. Instruction fetch unit 111determines the next instructions to be executed and recalls a fetchpacket sized set of such instructions. The nature and size of fetchpackets are further detailed below. Instructions are directly fetchedfrom L1I cache 121 upon a cache hit if the instructions are stored inL1I cache 121. Upon a cache miss occurring when the specifiedinstructions are not stored in L1I cache 121, the instructions aresought in L2 combined cache 130. In this example, the size of a cacheline in L1I cache 121 equals the size of a fetch packet which is 512bits. The memory locations of these instructions are either a hit in L2combined cache 130 or a miss. A hit is serviced from L2 combined cache130. A miss is serviced from a higher level of cache (not illustrated)or from main memory (not illustrated). In this example, the requestedinstruction is simultaneously supplied to both L1I cache 121 andprocessing unit core 110 to speed use.

In this example, processing unit core 110 includes multiple functionalunits to perform instruction specified data processing tasks.Instruction dispatch unit 112 determines the target functional unit ofeach fetched instruction. In this example, processing unit 110 operatesas a very long instruction word (VLIW) processor capable of operating onmultiple instructions in corresponding functional units simultaneously.A complier organizes instructions in execute packets that are executedtogether. Instruction dispatch unit 112 directs each instruction to itstarget functional unit. The functional unit assigned to an instructionis completely specified by the instruction produced by the compiler. Thehardware of processing unit core 110 has no part in the functional unitassignment. In this example, instruction dispatch unit 112 operates onseveral instructions in parallel. The number of such parallelinstructions is set by the size of the execute packet. This is furtherdescribed herein.

One part of the dispatch task of instruction dispatch unit 112 isdetermining whether the instruction is to execute on a functional unitin scalar data path side A 115 or vector data path side B 116. Aninstruction bit within each instruction called the s bit determineswhich data path the instruction controls. This is further describedherein.

Instruction decode unit 113 decodes each instruction in a currentexecute packet. Decoding includes identification of the functional unitperforming the instruction, identification of registers used to supplydata for the corresponding data processing operation from among possibleregister files, and identification of the register destination of theresults of the corresponding data processing operation. As furtherexplained below, instructions can include a constant field in place ofone register number operand field. The result of this decoding aresignals for control of the target functional unit to perform the dataprocessing operation specified by the corresponding instruction on thespecified data.

Processing unit core 110 includes control registers 114. Controlregisters 114 store information for control of the functional units inscalar data path side A 115 and vector data path side B 116. Thisinformation may include mode information or the like.

The decoded instructions from instruction decode 113 and informationstored in control registers 114 are supplied to scalar data path side A115 and vector data path side B 116. As a result, functional unitswithin scalar data path side A 115 and vector data path side B 116perform instruction specified data processing operations uponinstruction specified data and store the results in an instructionspecified data register or registers. Each of scalar data path side A115 and vector data path side B 116 includes multiple functional unitsthat operate in parallel. These are further described below inconjunction with FIG. 2. There is a data path 117 between scalar datapath side A 115 and vector data path side B 116 permitting dataexchange.

Processing unit core 110 includes further non-instruction-based modules.Emulation unit 118 permits determination of the machine state ofprocessing unit core 110 in response to instructions. This capabilitycan be employed for algorithmic development. Interrupts/exceptions unit119 enables processing unit core 110 to be responsive to external,asynchronous events (interrupts) and to respond to attempts to performimproper operations (exceptions).

Processor 100 includes streaming engine 125. Streaming engine 125supplies two data streams from predetermined addresses cached in L2combined cache 130 to register files of vector data path side B ofprocessing unit core 110. This provides controlled data movement frommemory (as cached in L2 combined cache 130) directly to functional unitoperand inputs. This is further described herein.

FIG. 1 illustrates example data widths of busses between various parts.L1I cache 121 supplies instructions to instruction fetch unit 111 viabus 141. Bus 141 is a 512-bit bus in this example. Bus 141 isunidirectional from L1I cache 121 to processing unit 110. L2 combinedcache 130 supplies instructions to L1I cache 121 via bus 142. Bus 142 isa 512-bit bus in this example. Bus 142 is unidirectional from L2combined cache 130 to L1I cache 121.

L1D cache 123 exchanges data with register files in scalar data pathside A 115 via bus 143. Bus 143 is a 64-bit bus in this example. L1Dcache 123 exchanges data with register files in vector data path side B116 via bus 144. Bus 144 is a 512-bit bus in this example. Busses 143and 144 are illustrated as bidirectional supporting both processing unitcore 110 data reads and data writes. L1D cache 123 exchanges data withL2 combined cache 130 via bus 145. Bus 145 is a 512-bit bus in thisexample. Bus 145 is illustrated as bidirectional supporting cacheservice for both processing unit core 110 data reads and data writes.

Processor data requests are directly fetched from L1D cache 123 upon acache hit (if the requested data is stored in L1D cache 123). Upon acache miss (the specified data is not stored in L1D cache 123), the datais sought in L2 combined cache 130. The memory locations of therequested data are either a hit in L2 combined cache 130 or a miss. Ahit is serviced from L2 combined cache 130. A miss is serviced fromanother level of cache (not illustrated) or from main memory (notillustrated). The requested data may be simultaneously supplied to bothL1D cache 123 and processing unit core 110 to speed use.

L2 combined cache 130 supplies data of a first data stream to streamingengine 125 via bus 146. Bus 146 is a 512-bit bus in this example.Streaming engine 125 supplies data of the first data stream tofunctional units of vector data path side B 116 via bus 147. Bus 147 isa 512-bit bus in this example. L2 combined cache 130 supplies data of asecond data stream to streaming engine 125 via bus 148. Bus 148 is a512-bit bus in this example. Streaming engine 125 supplies data of thissecond data stream to functional units of vector data path side B 116via bus 149, which is a 512-bit bus in this example. Busses 146, 147,148 and 149 are illustrated as unidirectional from L2 combined cache 130to streaming engine 125 and to vector data path side B 116 in accordancewith this example.

Streaming engine data requests are directly fetched from L2 combinedcache 130 upon a cache hit (if the requested data is stored in L2combined cache 130). Upon a cache miss (the specified data is not storedin L2 combined cache 130), the data is sought from another level ofcache (not illustrated) or from main memory (not illustrated). It istechnically feasible in some examples for L1D cache 123 to cache datanot stored in L2 combined cache 130. If such operation is supported,then upon a streaming engine data request that is a miss in L2 combinedcache 130, L2 combined cache 130 snoops L1D cache 123 for the streamingengine requested data. If L1D cache 123 stores the data, the snoopresponse includes the data, which is then supplied to service thestreaming engine request. If L1D cache 123 does not store the data, thesnoop response indicates this and L2 combined cache 130 services thestreaming engine request from another level of cache (not illustrated)or from main memory (not illustrated).

In this example, both L1D cache 123 and L2 combined cache 130 can beconfigured as selected amounts of cache or directly addressable memoryin accordance with U.S. Pat. No. 6,606,686 entitled Unified MemorySystem Architecture Including Cache and Directly Addressable StaticRandom Access Memory, which is incorporated by reference herein.

In this example, processor 100 is fabricated on an integrated chip (IC)that is mounted on a ball grid array (BGA) substrate. A BGA substrateand IC die together may be referred to as “BGA package,” “IC package,”“integrated circuit,” “IC,” “chip,” “microelectronic device,” or similarterminology. The BGA package may include encapsulation material to coverand protect the IC die from damage. In another example, other types ofknown or later developed packaging techniques may be used with processor100.

FIG. 2 illustrates further details of functional units and registerfiles within scalar data path side A 115 and vector data path side B116. Scalar data path side A 115 includes L1 unit 221, S1 unit 222, M1unit 223, N1 unit 224, D1 unit 225 and D2 unit 226. Scalar data pathside A 115 includes global scalar register file 211, L1/S1 localregister file 212, M1/N1 local register file 213 and D1/D2 localregister file 214. Vector data path side B 116 includes L2 unit 241, S2unit 242, M2 unit 243, N2 unit 244, C unit 245 and P unit 246. Vectordata path side B 116 includes global vector register file 231, L2/S2local register file 232, M2/N2/C local register file 233 and predicateregister file 234. Which functional units can read from or write towhich register files is described in more detail herein.

Scalar data path side A 115 includes L1 unit 221. L1 unit 221 generallyaccepts two 64-bit operands and produces one 64-bit result. The twooperands are each recalled from an instruction specified register ineither global scalar register file 211 or L1/S1 local register file 212.L1 unit 221 performs the following instruction selected operations:64-bit add/subtract operations; 32-bit min/max operations; 8-bit SingleInstruction Multiple Data (SIMD) instructions such as sum of absolutevalue, minimum and maximum determinations; circular min/max operations;and various move operations between register files. The result iswritten into an instruction specified register of global scalar registerfile 211, L1/S1 local register file 212, M1/N1 local register file 213or D1/D2 local register file 214.

Scalar data path side A 115 includes S1 unit 222. S1 unit 222 generallyaccepts two 64-bit operands and produces one 64-bit result. The twooperands are each recalled from an instruction specified register ineither global scalar register file 211 or L1/S1 local register file 212.In this example, S1 unit 222 performs the same type operations as L1unit 221. In another example, there may be slight variations between thedata processing operations supported by L1 unit 221 and S1 unit 222. Theresult is written into an instruction specified register of globalscalar register file 211, L1/S1 local register file 212, M1/N1 localregister file 213 or D1/D2 local register file 214.

Scalar data path side A 115 includes M1 unit 223. M1 unit 223 generallyaccepts two 64-bit operands and produces one 64-bit result. The twooperands are each recalled from an instruction specified register ineither global scalar register file 211 or M1/N1 local register file 213.Examples of the instruction selected operations performed by the exampleM1 unit 223 include 8-bit, 16-bit, and 32-bit multiply operations,Galois field multiplication, complex multiplication with and withoutrounding, IEEE floating point multiply operations, complex dot productoperations, 32-bit bit count operations, complex conjugate multiplyoperations, and bit-wise logical operations, moves, adds and subtracts.The result is written into an instruction specified register of globalscalar register file 211, L1/S1 local register file 212, M1/N1 localregister file 213 or D1/D2 local register file 214.

Scalar data path side A 115 includes N1 unit 224. N1 unit 224 generallyaccepts two 64-bit operands and produces one 64-bit result. The twooperands are each recalled from an instruction specified register ineither global scalar register file 211 or M1/N1 local register file 213.In this example, N1 unit 224 performs the same type operations as M1unit 223. There are also double operations referred to as dual issueinstructions that employ both the M1 unit 223 and the N1 unit 224together. The result is written into an instruction specified registerof global scalar register file 211, L1/S1 local register file 212, M1/N1local register file 213 or D1/D2 local register file 214.

Scalar data path side A 115 includes D1 unit 225 and D2 unit 226. D1unit 225 and D2 unit 226 generally each accept two 64-bit operands andeach produce one 64-bit result. D1 unit 225 and D2 unit 226 generallyperform address calculations and corresponding load and storeoperations. D1 unit 225 is used for scalar loads and stores of 64 bits.D2 unit 226 is used for vector loads and stores of 512 bits. In thisexample, D1 unit 225 and D2 unit 226 also perform: swapping, pack andunpack on the load and store data; 64-bit SIMD arithmetic operations;and 64-bit bit-wise logical operations. D1/D2 local register file 214stores base and offset addresses used in address calculations for thecorresponding loads and stores. The two operands are each recalled froman instruction specified register in either global scalar register file211 or D1/D2 local register file 214. The calculated result is writteninto an instruction specified register of global scalar register file211, L1/S1 local register file 212, M1/N1 local register file 213 orD1/D2 local register file 214.

Vector data path side B 116 includes L2 unit 241. L2 unit 241 generallyaccepts two 512-bit operands and produces one 512-bit result. The twooperands are each recalled from an instruction specified register ineither global vector register file 231, L2/S2 local register file 232 orpredicate register file 234. In this example, L2 unit 241 performsinstructions similar to L1 unit 221 except on wider 512-bit data. Theresult may be written into an instruction specified register of globalvector register file 231, L2/S2 local register file 232, M2/N2/C localregister file 233 or predicate register file 234.

Vector data path side B 116 includes S2 unit 242. S2 unit 242 generallyaccepts two 512-bit operands and produces one 512-bit result. The twooperands are each recalled from an instruction specified register ineither global vector register file 231, L2/S2 local register file 232 orpredicate register file 234. In this example, S2 unit 242 performsinstructions similar to S1 unit 222. The result is written into aninstruction specified register of global vector register file 231, L2/S2local register file 232, M2/N2/C local register file 233 or predicateregister file 234.

Vector data path side B 116 includes M2 unit 243. M2 unit 243 generallyaccepts two 512-bit operands and produces one 512-bit result. The twooperands are each recalled from an instruction specified register ineither global vector register file 231 or M2/N2/C local register file233. In this example, M2 unit 243 performs instructions similar to M1unit 223 except on wider 512-bit data. The result is written into aninstruction specified register of global vector register file 231, L2/S2local register file 232 or M2/N2/C local register file 233.

Vector data path side B 116 includes N2 unit 244. N2 unit 244 generallyaccepts two 512-bit operands and produces one 512-bit result. The twooperands are each recalled from an instruction specified register ineither global vector register file 231 or M2/N2/C local register file233. In this example, N2 unit 244 performs the same type operations asM2 unit 243. There are also double operations referred to as dual issueinstructions that employ both M2 unit 243 and the N2 unit 244 together.The result is written into an instruction specified register of globalvector register file 231, L2/S2 local register file 232 or M2/N2/C localregister file 233.

Vector data path side B 116 includes correlation (C) unit 245. C unit245 generally accepts two 512-bit operands and produces one 512-bitresult. The two operands are each recalled from an instruction specifiedregister in either global vector register file 231 or M2/N2/C localregister file 233. In this example, C unit 245 performs “Rake” and“Search” instructions that are used for WCDMA (wideband code divisionmultiple access) encoding/decoding. In this example, C unit 245 canperform up to 512 multiplies per clock cycle of a 2-bit PN (pseudorandomnumber) and 8-bit I/Q (complex number), 8-bit and 16-bitSum-of-Absolute-Difference (SAD) calculations, up to 512 SADs per clockcycle, horizontal add and horizontal min/max instructions, and vectorpermutes instructions. C unit 245 also contains 4 vector controlregisters (CUCR0 to CUCR3) used to control certain operations of C unit245 instructions. Control registers CUCR0 to CUCR3 are used as operandsin certain C unit 245 operations. In some examples, control registersCUCR0 to CUCR3 are used in control of a general permutation instruction(VPERM), and as masks for SIMD multiple DOT product operations (DOTPM)and SIMD multiple Sum-of-Absolute-Difference (SAD) operations. Infurther examples, control register CUCR0 is used to store thepolynomials for Galois Field Multiply operations (GFMPY) and controlregister CUCR1 is used to store the Galois field polynomial generatorfunction.

Vector data path side B 116 includes P unit 246. Vector predicate (P)unit 246 performs basic logic operations on registers of local predicateregister file 234. P unit 246 has direct access to read from and writeto predication register file 234. The logic operations include singleregister unary operations such as NEG (negate) which inverts each bit ofthe single register, BITCNT (bit count) which returns a count of thenumber of bits in the single register having a predetermined digitalstate (1 or 0), RMBD (right most bit detect) which returns a number ofbit positions from the least significant bit position (right most) to afirst bit position having a predetermined digital state (1 or 0),DECIMATE which selects every instruction specified Nth (1, 2, 4, etc.)bit to output, and EXPAND which replicates each bit an instructionspecified N times (2, 4, etc.). The logic operations also include tworegister binary operations such as AND which is a bitwise AND of data ofthe two registers, NAND which is a bitwise AND and negate of data of thetwo registers, OR which is a bitwise OR of data of the two registers,NOR which is a bitwise OR and negate of data of the two registers, andXOR which is exclusive OR of data of the two registers. The logicoperations include transfer of data from a predicate register ofpredicate register file 234 to another specified predicate register orto a specified data register in global vector register file 231. One useof P unit 246 is manipulation of the SIMD vector comparison results foruse in control of a further SIMD vector operation. The BITCNTinstruction can be used to count the number of 1's in a predicateregister to determine the number of valid data elements from a predicateregister.

FIG. 3 illustrates global scalar register file 211. There are 16independent 64-bit wide scalar registers designated A0 to A15. Eachregister of global scalar register file 211 can be read from or writtento as 64-bits of scalar data. All scalar data path side A 115 functionalunits (L1 unit 221, S1 unit 222, M1 unit 223, N1 unit 224, D1 unit 225and D2 unit 226) can read or write to global scalar register file 211.Global scalar register file 211 can be read from as 32-bits or as64-bits and written to as 64-bits. The instruction executing determinesthe read data size. Vector data path side B 116 functional units (L2unit 241, S2 unit 242, M2 unit 243, N2 unit 244, C unit 245 and P unit246) can read from global scalar register file 211 via cross path 117under restrictions that are described below.

FIG. 4 illustrates D1/D2 local register file 214. There are sixteenindependent 64-bit wide scalar registers designated D0 to D16. Eachregister of D1/D2 local register file 214 is read from or written to as64-bits of scalar data. All scalar data path side A 115 functional units(L1 unit 221, S1 unit 222, M1 unit 223, N1 unit 224, D1 unit 225 and D2unit 226) can write to global scalar register file 211. Only D1 unit 225and D2 unit 226 can read from D1/D2 local scalar register file 214. Datastored in D1/D2 local scalar register file 214 can include baseaddresses and offset addresses used in address calculation.

FIG. 5 illustrates L1/S1 local register file 212. In this example, L1/S1local register file 212 includes eight independent 64-bit wide scalarregisters designated AL0 to AL7. In this example, the instruction codingpermits L1/S1 local register file 212 to include up to 16 registers. Inthis example, eight registers are implemented to reduce circuit size andcomplexity. Each register of L1/S1 local register file 212 can be readfrom or written to as 64-bits of scalar data. All scalar data path sideA 115 functional units (L1 unit 221, S1 unit 222, M1 unit 223, N1 unit224, D1 unit 225 and D2 unit 226) can write to L1/S1 local scalarregister file 212. L1 unit 221 and S1 unit 222 can read from L1/S1 localscalar register file 212.

FIG. 6 illustrates M1/N1 local register file 213. In this example, eightindependent 64-bit wide scalar registers designated AM0 to AM7 areimplemented. In this example, the instruction coding permits M1/N1 localregister file 213 to include up to 16 registers. In this example, eightregisters are implemented to reduce circuit size and complexity. Eachregister of M1/N1 local register file 213 can be read from or written toas 64-bits of scalar data. All scalar data path side A 115 functionalunits (L1 unit 221, S1 unit 222, M1 unit 223, N1 unit 224, D1 unit 225and D2 unit 226) can write to M1/N1 local scalar register file 213. M1unit 223 and N1 unit 224 can read from M1/N1 local scalar register file213.

FIG. 7 illustrates global vector register file 231. There are sixteenindependent 512-bit wide vector registers. Each register of globalvector register file 231 can be read from or written to as 64-bits ofscalar data designated B0 to B15. Each register of global vectorregister file 231 can be read from or written to as 512-bits of vectordata designated VB0 to VB15. The instruction type determines the datasize. All vector data path side B 116 functional units (L2 unit 241, S2unit 242, M2 unit 243, N2 unit 244, C unit 245 and P unit 246) can reador write to global vector register file 231. Scalar data path side A 115functional units (L1 unit 221, S1 unit 222, M1 unit 223, N1 unit 224, D1unit 225 and D2 unit 226) can read from global vector register file 231via cross path 117 under restrictions that are described below.

FIG. 8 illustrates predicate (P) local register file 234. There areeight independent 64-bit wide registers designated P0 to P7. Eachregister of P local register file 234 can be read from or written to as64-bits of scalar data. Vector data path side B 116 functional units L2unit 241, S2 unit 242, C unit 244 and P unit 246 can write to P localregister file 234. L2 unit 241, S2 unit 242 and P unit 246 can read fromP local scalar register file 234. One use of P local register file 234is writing one-bit SIMD vector comparison results from L2 unit 241, S2unit 242 or C unit 244, manipulation of the SIMD vector comparisonresults by P unit 246, and use of the manipulated results in control ofa further SIMD vector operation.

FIG. 9 illustrates L2/S2 local register file 232. In this example, eightindependent 512-bit wide vector registers are implemented. In thisexample, the instruction coding permits L2/S2 local register file 232 toinclude up to sixteen registers. In this example, eight registers areimplemented to reduce circuit size and complexity. Each register ofL2/S2 local vector register file 232 can be read from or written to as64-bits of scalar data designated BL0 to BL7. Each register of L2/S2local vector register file 232 can be read from or written to as512-bits of vector data designated VBL0 to VBL7. The instruction typedetermines the data size. All vector data path side B 116 functionalunits (L2 unit 241, S2 unit 242, M2 unit 243, N2 unit 244, C unit 245and P unit 246) can write to L2/S2 local vector register file 232. L2unit 241 and S2 unit 242 can read from L2/S2 local vector register file232.

FIG. 10 illustrates M2/N2/C local register file 233. In this example,eight independent 512-bit wide vector registers are implemented. In thisexample, the instruction coding permits M2/N2/C local register file 233to include up to sixteen registers. In this example, eight registers areimplemented to reduce circuit size and complexity. Each register ofM2/N2/C local vector register file 233 can be read from or written to as64-bits of scalar data designated BM0 to BM7. Each register of M2/N2/Clocal vector register file 233 can be read from or written to as512-bits of vector data designated VBM0 to VBM7. All vector data pathside B 116 functional units (L2 unit 241, S2 unit 242, M2 unit 243, N2unit 244, C unit 245 and P unit 246) can write to M2/N2/C local vectorregister file 233. M2 unit 243, N2 unit 244 and C unit 245 can read fromM2/N2/C local vector register file 233.

The provision of global register files accessible by all functionalunits of a side and local register files accessible by some of thefunctional units of a side is a design choice. In another example, adifferent accessibility provision could be made, such as employing onetype of register file corresponding to the global register filesdescribed herein.

Cross path 117 permits limited exchange of data between scalar data pathside A 115 and vector data path side B 116. During each operationalcycle one 64-bit data word can be recalled from global scalar registerfile A 211 for use as an operand by one or more functional units ofvector data path side B 116 and one 64-bit data word can be recalledfrom global vector register file 231 for use as an operand by one ormore functional units of scalar data path side A 115. Any scalar datapath side A 115 functional unit (L1 unit 221, S1 unit 222, M1 unit 223,N1 unit 224, D1 unit 225 and D2 unit 226) can read a 64-bit operand fromglobal vector register file 231. This 64-bit operand is the leastsignificant bits of the 512-bit data in the accessed register of globalvector register file 231. Multiple scalar data path side A 115functional units can employ the same 64-bit cross path data as anoperand during the same operational cycle. In one example, a single64-bit operand is transferred from vector data path side B 116 to scalardata path side A 115 in a single operational cycle. Any vector data pathside B 116 functional unit (L2 unit 241, S2 unit 242, M2 unit 243, N2unit 244, C unit 245 and P unit 246) can read a 64-bit operand fromglobal scalar register file 211. If the corresponding instruction is ascalar instruction, the cross-path operand data is treated as a 64-bitoperand. If the corresponding instruction is a vector instruction, theupper 448 bits of the operand are zero filled. Multiple vector data pathside B 116 functional units can employ the same 64-bit cross path dataas an operand during the same operational cycle. In one example, asingle 64-bit operand is transferred from scalar data path side A 115 tovector data path side B 116 in a single operational cycle.

Streaming engine 125 (FIG. 1) transfers data in certain restrictedcircumstances. Streaming engine 125 controls two data streams. A streamincludes of a sequence of elements of a particular type. Programs thatoperate on streams read the data sequentially, operating on each elementin turn. Every stream has the following basic properties: the streamdata have a well-defined beginning and ending in time; the stream datahave fixed element size and type throughout the stream; and, the streamdata have a fixed sequence of elements. Once a stream is opened,streaming engine 125 performs the following operations: calculates theaddress; fetches the defined data type from L2 unified cache 130 (whichmay require cache service from a higher level memory, e.g., in the eventof a cache miss in L2); performs data type manipulation such as zeroextension, sign extension, data element sorting/swapping such as matrixtransposition; and delivers the data directly to the programmed dataregister file within processor core 110. Streaming engine 125 is thususeful for real-time digital filtering operations on well-behaved data.Streaming engine 125 frees the corresponding processor from these memoryfetch tasks, thus enabling other processing functions.

Streaming engine 125 provides several benefits. For example, streamingengine 125 permits multi-dimensional memory accesses, increases theavailable bandwidth to the functional units minimizes the number ofcache miss stalls since the stream buffer bypasses L1D cache 123, andreduces the number of scalar operations required to maintain a loop.Streaming engine 125 also manages address pointers and handles addressgeneration which frees up the address generation instruction slots andD1 unit 225 and D2 unit 226 for other computations.

Processor core 110 (FIG. 1) operates on an instruction pipeline.Instructions are fetched in instruction packets of fixed length asfurther described below. All instructions require the same number ofpipeline phases for fetch and decode but require a varying number ofexecute phases.

FIG. 11 illustrates the following pipeline phases: program fetch phase1110, dispatch and decode phases 1120, and execution phases 1130.Program fetch phase 1110 includes three stages for all instructions.Dispatch and decode phases 1120 include three stages for allinstructions. Execution phase 1130 includes one to four stages dependingon the instruction.

Fetch phase 1110 includes program address generation (PG) stage 1111,program access (PA) stage 1112 and program receive (PR) stage 1113.During program address generation stage 1111, the program address isgenerated in the processor and the read request is sent to the memorycontroller for the L1I cache. During the program access stage 1112, theL1I cache processes the request, accesses the data in its memory andsends a fetch packet to the processor boundary. During the programreceive stage 1113, the processor registers the fetch packet.

Instructions are fetched in a fetch packet that includes sixteen 32-bitwide words. FIG. 12 illustrates sixteen instructions 1201 to 1216 of asingle fetch packet. Fetch packets are aligned on 512-bit (16-word)boundaries. This example employs a fixed 32-bit instruction length whichenables decoder alignment. A properly aligned instruction fetch can loadmultiple instructions into parallel instruction decoders. Such aproperly aligned instruction fetch can be achieved by predeterminedinstruction alignment when stored in memory by having fetch packetsaligned on 512-bit boundaries coupled with a fixed instruction packetfetch. Conversely, variable length instructions require an initial stepof locating each instruction boundary before decoding. A fixed lengthinstruction set generally permits more regular layout of instructionfields which simplifies the construction of each decoder which is anadvantage for a wide issue VLIW processor.

The execution of the individual instructions is partially controlled bya p bit in each instruction. In this example, the p bit is bit 0 of the32-bit wide slot. The p bit determines whether an instruction executesin parallel with the next instruction. In this example, instructions arescanned from lower to higher address. If the p bit of an instruction is1, then the next following instruction (higher memory address) isexecuted in parallel with (in the same cycle as) that instruction. Ifthe p bit of an instruction is 0, then the next following instruction isexecuted in the cycle after the instruction.

Processor core 110 (FIG. 1) and L1I cache 121 pipelines (FIG. 1) arede-coupled from each other. Fetch packet returns from L1I cache can takea different number of clock cycles, depending on external circumstancessuch as whether there is a hit in L1I cache 121 or a hit in L2 combinedcache 130. Therefore, program access stage 1112 can take several clockcycles instead of one clock cycle as in the other stages.

The instructions executing in parallel constitute an execute packet. Inthis example, an execute packet can contain up to sixteen 32-bit wideslots for sixteen instructions. No two instructions in an execute packetcan use the same functional unit. A slot is one of five types: 1) aself-contained instruction executed on one of the functional units ofprocessor core 110 (L1 unit 221, S1 unit 222, M1 unit 223, N1 unit 224,D1 unit 225, D2 unit 226, L2 unit 241, S2 unit 242, M2 unit 243, N2 unit244, C unit 245 and P unit 246); 2) a unitless instruction such as a NOP(no operation) instruction or multiple NOP instructions; 3) a branchinstruction; 4) a constant field extension; and 5) a conditional codeextension. Some of these slot types are further explained herein.

Dispatch and decode phases 1120 (FIG. 11) include instruction dispatchto appropriate execution unit (DS) stage 1121, instruction pre-decode(DC1) stage 1122, and instruction decode, operand read (DC2) stage 1123.During instruction dispatch to appropriate execution unit stage 1121,the fetch packets are split into execute packets and assigned to theappropriate functional units. During the instruction pre-decode stage1122, the source registers, destination registers, and associated pathsare decoded for the execution of the instructions in the functionalunits. During the instruction decode, operand read stage 1123, moredetailed unit decodes are performed and operands are read from theregister files.

Execution phase 1130 includes execution (E1 to E5) stages 1131 to 1135.Different types of instructions require different numbers of such stagesto complete execution. The execution stages of the pipeline play animportant role in understanding the device state at processor cycleboundaries.

During E1 stage 1131, the conditions for the instructions are evaluatedand operands are operated on. As illustrated in FIG. 11, E1 stage 1131can receive operands from a stream buffer 1141 and one of the registerfiles shown schematically as 1142. For load and store instructions,address generation is performed, and address modifications are writtento a register file. For branch instructions, the branch fetch packet inPG phase is affected. As illustrated in FIG. 11, load and storeinstructions access memory here shown schematically as memory 1151. Forsingle-cycle instructions, results are written to a destination registerfile when any conditions for the instructions are evaluated as true. Ifa condition is evaluated as false, the instruction does not write anyresults or have any pipeline operation after E1 stage 1131.

During E2 stage 1132, load instructions send the address to memory.Store instructions send the address and data to memory. Single-cycleinstructions that saturate results set the SAT bit in the control statusregister (CSR) if saturation occurs. For 2-cycle instructions, resultsare written to a destination register file.

During E3 stage 1133, data memory accesses are performed. Any multiplyinstructions that saturate results set the SAT bit in the control statusregister (CSR) if saturation occurs. For 3-cycle instructions, resultsare written to a destination register file.

During E4 stage 1134, load instructions bring data to the processorboundary. For 4-cycle instructions, results are written to a destinationregister file.

During E5 stage 1135, load instructions write data into a register asillustrated schematically in FIG. 11 with input from memory 1151 to E5stage 1135.

FIG. 13 illustrates an example of instruction coding 1300 used byprocessing unit core 110. The illustrated instruction format is for atwo source arithmetic instruction. Other instruction coding may also beused. In general, instructions include 32 bits and control the operationof one of the individually controllable functional units (L1 unit 221,S1 unit 222, M1 unit 223, N1 unit 224, D1 unit 225, D2 unit 226, L2 unit241, S2 unit 242, M2 unit 243, N2 unit 244, C unit 245 and P unit 246).

In the example of FIG. 13, the dst field 1301 specifies a register in acorresponding register file as the destination of the instructionresults. The src2/cst field 1302 (bits 18 to 22) has several meaningsdepending on the instruction opcode field 1304 and the unit field 1305.One meaning specifies a register of a corresponding register file as thesecond operand.

Another meaning is an immediate constant. Depending on the instructiontype, the field 1302 is treated as an unsigned integer and zero extendedto a specified data length or is treated as a signed integer and signextended to the specified data length.

The src1 field 1303 specifies a register in a corresponding registerfile as the first source operand. The opcode field 1304 specifies thetype of instruction. The unit field 1305 in combination with the sidebit (“s” bit) 1306 indicates which of the functional units to be used toexecute the instruction. A detailed explanation of the opcode is beyondthe scope of this description except for the instruction optionsdescribed below.

The s bit 1306 designates scalar data path side A 115 or vector datapath side B 116. If s=0, then scalar data path side A 115 is selectedwhich limits the functional unit to L1 unit 221, S1 unit 222, M1 unit223, N1 unit 224, D1 unit 225 and D2 unit 226 and the correspondingregister files illustrated in FIG. 2. Similarly, s=1 selects vector datapath side B 116 which limits the functional unit to L2 unit 241, S2 unit242, M2 unit 243, N2 unit 244, P unit 246 and the corresponding registerfiles illustrated in FIG. 2.

The p bit 1307 marks the execute packets. The p-bit determines whetherthe instruction executes in parallel with the following instruction. Thep-bits are scanned from lower to higher address. If p=1 for the currentinstruction, then the next instruction executes in parallel with thecurrent instruction. If p=0 for the current instruction, then the nextinstruction executes in the cycle after the current instruction. Allinstructions executing in parallel constitute an execute packet. Anexecute packet can contain up to sixteen instructions. Each instructionin an execute packet uses a different functional unit.

Most instructions of the processing unit core 110 do not include directencoding for conditional execution. However, instructions can be madeconditional. The act of making an instruction conditional is calledpredication and the register storing the condition is referred to as apredicate register. An execute packet can include two 32-bit conditioncode extension slots which encode 4-bit condition information forinstructions in the same execute packet. The condition code slots arereferred to as condition code extension slot 0 and condition codeextension slot 1 and the 4-bit condition information is referred to as acreg/z field herein.

Table 1 shows the encodings of a creg/z field. The creg bits identifythe predicate register and the z bit indicates whether the predicationis based on zero or not zero in the predicate register. Execution of aconditional instruction is conditional upon the value stored in thespecified data register. If z=1, the test is for equality with zero. Ifz=0, the test is for nonzero. The case of creg=0 and z=0 is treated astrue to allow unconditional instruction execution. Note that “z” in thez bit column refers to the zero/not zero comparison selection notedabove and “x” is a don't care state.

TABLE 1 Meaning creg z Unconditional 0 0 0 0 Reserved 0 0 0 1 A0 0 0 1 zA1 0 1 0 z A2 0 1 1 z A3 1 0 0 z A4 1 0 1 z A5 1 1 0 z Reserved 1 1 x x

FIG. 14 illustrates the coding for condition code extension slot 0 1400.Field 1401 specifies four creg/z bits assigned to the L1 unit 221instruction, field 1402 specifies four creg/z bits assigned to the L2unit 241 instruction, field 1403 specifies four creg/z bits assigned tothe S1 unit 222 instruction, field 1404 specifies four creg/z bitsassigned to the S2 unit 242 instruction, field 1405 specifies fourcreg/z bits assigned to the D1 unit 225 instruction, field 1406specifies four creg/z bits assigned to the D2 unit 226 instruction,field 1407 is unused/reserved, and field 1408 is coded as a set ofunique bits (CCEX0) that identify the condition code extension slot 0.When the unique ID of condition code extension slot 0 is detected, thecreg/z bits are employed to control conditional execution of anycorresponding L1 unit 221, L2 unit 241, S1 unit 222, S2 unit 242, D1unit 225, and D2 unit 226 instruction in the same execution packet. Notethat a properly coded condition code extension slot 0 can make someinstructions in an execute packet conditional and some unconditional.

FIG. 15 illustrates the coding for condition code extension slot 1 1500.Field 1501 specifies four creg/z bits assigned to the M1 unit 223instruction, field 1502 specifies four creg/z bits assigned to the M2unit 243 instruction, field 1503 specifies four creg/z bits assigned tothe C unit 245 instruction, field 1504 specifies four creg/z bitsassigned to the N1 unit 224 instruction, field 1505 specifies fourcreg/z bits assigned to the N2 unit 244 instruction, field 1506 isunused/reserved, and field 1507 is coded as a set of unique bits (CCEX1)that identify the condition code extension slot 1. When the unique ID ofcondition code extension slot 1 is detected, the corresponding creg/zbits are employed to control conditional execution of any M1 unit 223,M2 unit 243, C unit 245, N1 unit 224 and N2 unit 244 instruction in thesame execution packet.

Referring again to FIG. 13, in some instructions, a bit in the opcodefield 1304 referred to as the constant extension bit can be encoded toindicate that a constant in the src2/CST field 1302 is to be extended.An execute packet can include two 32-bit constant extension slots thatcan each store 27-bits to be concatenated as high order bits with a5-bit constant in the field 1302 to form a 32-bit constant. FIG. 16illustrates the fields of constant extension slot 0 1600. Field 1601stores the most significant 27 bits of an extended 32-bit constant.Field 1602 is coded as a set of unique bits (CSTX0) to identify theconstant extension slot 0. In this example, constant extension slot 01600 can be used to extend the constant of one of an L1 unit 221instruction, data in a D1 unit 225 instruction, an S2 unit 242instruction, an offset in a D2 unit 226 instruction, an M2 unit 243instruction, an N2 unit 244 instruction, a branch instruction, or a Cunit 245 instruction in the same execute packet. Constant extension slot1 is similar to constant extension slot 0 except the slot is coded witha set of unique bits (CSTX1) to identify the constant extension slot 1.In this example, constant extension slot 1 can be used to extend theconstant of one of an L2 unit 241 instruction, data in a D2 unit 226instruction, an S1 unit 222 instruction, an offset in a D1 unit 225instruction, an M1 unit 223 instruction or an N1 unit 224 instruction inthe same execute packet.

Constant extension slot 0 and constant extension slot 1 are used asfollows. Instruction decoder 113 determines that a constant is in field1302, referred to as an immediate field, from the instruction opcodebits and whether or not the constant is to be extended from thepreviously mentioned constant extension bit in the opcode field 1304. Ifinstruction decoder 113 detects a constant extension slot 0 or aconstant extension slot 1, instruction decoder 113 checks theinstructions within the execute packet for an instruction correspondingto the detected constant extension slot. A constant extension is made ifone corresponding instruction has a constant extension bit equal to 1.

FIG. 17 is a partial block diagram 1700 illustrating constant extension.FIG. 17 assumes that instruction decoder 113 (FIG. 1) detects a constantextension slot and a corresponding instruction in the same executepacket. Instruction decoder 113 supplies the twenty-seven extension bitsfrom field 1601 of the constant extension slot and the five constantbits from field 1302 from the corresponding instruction to concatenator1701. Concatenator 1701 forms a single 32-bit word from these two parts.In this example, the twenty-seven extension bits from field 1601 of theconstant extension slot are the most significant bits and the fiveconstant bits from field 1302 are the least significant bits. Thecombined 32-bit word is supplied to one input of multiplexer 1702. Thefive constant bits from the corresponding instruction field 1302 supplya second input to multiplexer 1702. Selection of multiplexer 1702 iscontrolled by the status of the constant extension bit. If the constantextension bit is 1, multiplexer 1702 selects the concatenated 32-bitinput. If the constant extension bit is 0, multiplexer 1702 selects thefive constant bits from the corresponding instruction field 1302. Theoutput of multiplexer 1702 supplies an input of sign extension unit1703.

Sign extension unit 1703 forms the final operand value from the inputfrom multiplexer 1703. Sign extension unit 1703 receives control inputsscalar/vector and data size. The scalar/vector input indicates whetherthe corresponding instruction is a scalar instruction or a vectorinstruction. The functional units of data path side A 115 (L1 unit 221,S1 unit 222, M1 unit 223, N1 unit 224, D1 unit 225 and D2 unit 226)perform scalar instructions. Any instruction directed to one of thesefunctional units is a scalar instruction. Data path side B functionalunits L2 unit 241, S2 unit 242, M2 unit 243, N2 unit 244 and C unit 245can perform scalar instructions or vector instructions. Instructiondecoder 113 determines whether the instruction is a scalar instructionor a vector instruction from the opcode bits. P unit 246 may performscalar instructions. The data size can be eight bits (byte B), sixteenbits (half-word H), 32 bits (word W), or 64 bits (double word D). Table2 lists the operation of sign extension unit 1703 for the variousoptions.

TABLE 2 Instruction Operand Constant Type Size Length Action ScalarB/H/W/D  5 bits Sign extend to 64 bits Scalar B/H/W/D 32 bits Signextend to 64 bits Vector B/H/W/D  5 bits Sign extend to operand size andreplicate across whole vector Vector B/H/W 32 bits Replicate 32-bitconstant across each 32-bit (W) lane Vector D 32 bits Sign extend to 64bits and replicate across each 64-bit (D) lane

An execute packet can include a constant extension slot 0 or 1 and morethan one corresponding instruction marked constant extended. For such anoccurrence, for constant extension slot 0, more than one of an L1 unit221 instruction, data in a D1 unit 225 instruction, an S2 unit 242instruction, an offset in a D2 unit 226 instruction, an M2 unit 243instruction or an N2 unit 244 instruction in an execute packet canindicate constant extension. For such an occurrence, for constantextension slot 1, more than one of an L2 unit 241 instruction, data in aD2 unit 226 instruction, an S1 unit 222 instruction, an offset in a D1unit 225 instruction, an M1 unit 223 instruction or an N1 unit 224instruction in an execute packet can indicate constant extension. In oneexample, instruction decoder 113 determines that such an occurrence isan invalid operation and not supported. Alternately, the combination canbe supported with extension bits of the constant extension slot appliedto each corresponding functional unit instruction marked constantextended.

L1 unit 221, S1 unit 222, L2 unit 241, S2 unit 242 and C unit 245 oftenoperate in a single instruction multiple data (SIMD) mode. In this SIMDmode, the same instruction is applied to packed data from the twooperands. Each operand holds multiple data elements disposed inpredetermined slots. SIMD operation is enabled by carry control at thedata boundaries. Such carry control enables operations on varying datawidths.

FIG. 18 illustrates the carry control logic. AND gate 1801 receives thecarry output of bit N within the operand wide arithmetic logic unit (64bits for scalar data path side A 115 functional units and 512 bits forvector data path side B 116 functional units). AND gate 1801 alsoreceives a carry control signal which is further explained below. Theoutput of AND gate 1801 is supplied to the carry input of bit N+1 of theoperand wide arithmetic logic unit. AND gates such as AND gate 1801 aredisposed between every pair of bits at a possible data boundary. Forexample, for 8-bit data such an AND gate will be between bits 7 and 8,bits 15 and 16, bits 23 and 24, etc. Each such AND gate receives acorresponding carry control signal. If the data size is the minimumsize, each carry control signal is 0, effectively blocking carrytransmission between the adjacent bits. The corresponding carry controlsignal is 1 if the selected data size requires both arithmetic logicunit sections. Table 3 below shows example carry control signals for thecase of a 512-bit wide operand as used by vector data path side B 116functional units which can be divided into sections of 8 bits, 16 bits,32 bits, 64 bits, 128 bits or 256 bits. In Table 3, the upper 32 bitscontrol the upper bits (bits 128 to 511) carries and the lower 32 bitscontrol the lower bits (bits 0 to 127) carries. No control of the carryoutput of the most significant bit is needed, thus only 63 carry controlsignals are required.

TABLE 3 Data Size Carry Control Signals  8 bits (B) −000 0000 0000 00000000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000  16 bits (H)−101 0101 0101 0101 0101 0101 0101 0101 0101 0101 0101 0101 0101 01010101 0101  32 bits (W) −111 0111 0111 0111 0111 0111 0111 0111 0111 01110111 0111 0111 0111 0111 0111  64 bits (D) −111 1111 0111 1111 0111 11110111 1111 0111 1111 0111 1111 0111 1111 0111 1111 128 bits −111 11111111 1111 0111 1111 1111 1111 0111 1111 1111 1111 0111 1111 1111 1111256 bits −111 1111 1111 1111 1111 1111 1111 1111 0111 1111 1111 11111111 1111 1111 1111

Operation on data sizes that are integer powers of 2 (2^(N)) is common.However, the carry control technique is not limited to integer powers of2 and can be applied to other data sizes and operand widths.

In this example, at least L2 unit 241 and S2 unit 242 employ two typesof SIMD instructions using registers in predicate register file 234. Inthis example, the SIMD vector predicate instructions operate on aninstruction specified data size. The data sizes include byte (8 bit)data, half word (16 bit) data, word (32 bit) data, double word (64 bit)data, quad word (128 bit) data and half vector (256 bit) data. In thefirst of these instruction types, the functional unit (L2 unit 241 or S2unit 242) performs a SIMD comparison on packed data in two general dataregisters and supplies results to a predicate data register. Theinstruction specifies a data size, the two general data registeroperands, and the destination predicate register. In this example, eachpredicate data register includes one bit corresponding to each minimaldata size portion of the general data registers. In the current example,the general data registers are 512 bits (64 bytes) and the predicatedata registers are 64 bits (8 bytes). Each bit of a predicate dataregister corresponds to eight bits of a general data register. Thecomparison is performed on a specified data size (8, 16, 32, 64, 128 or256 bits). If the comparison is true, then the functional unit supplies1's to all predicate register bits corresponding to that data sizeportion. If the comparison is false, the functional unit supplies zeroesto the predicate register bits corresponding to that data size portion.In this example, the enabled comparison operations include: less than,greater than, and equal to.

In the second of the instruction types, the functional unit (L2 unit 241or S2 unit 242) separately performs a first SIMD operation or a secondSIMD operation on packed data in general data registers based upon thestate of data in a predicate data register. The instruction specifies adata size, one or two general data register operands, a controllingpredicate register, and a general data register destination. Forexample, a functional unit can select, for each data sized portion oftwo vector operands, a first data element of a first operand or a seconddata element of a second operand dependent upon the 1/0 state ofcorresponding bits in the predicate data register to store in thedestination register. In another example, the data elements of a singlevector operand can be saved to memory or not saved dependent upon thedata of the corresponding bits of the predicate register.

The operations of P unit 245 permit a variety of compound vector SIMDoperations based upon more than one vector comparison. For example, arange determination can be made using two comparisons. In a SIMDoperation, a candidate vector is compared with a vector reference havingthe minimum of the range packed within a data register. The greater thanresult is scalar data with bits corresponding to the SIMD data width setto 0 or 1 depending upon the SIMD comparison and is stored in apredicate data register. Another SIMD comparison of the candidate vectoris performed with another reference vector having the maximum of therange packed within a different data register produces another scalarwith less than results stored in another predicate register. The P unitthen ANDs the two predicate registers. The AND result indicates whethereach SIMD data part of the candidate vector is within range or out ofrange. A P unit BITCNT instruction of the AND result can produce a countof the data elements within the comparison range. The P unit NEGfunction can be used to convert: a less than comparison result to agreater than or equal comparison result; a greater than comparisonresult to a less than or equal to comparison result; or, an equal tocomparison result to a not equal to comparison result.

FIG. 19 is a conceptual view of the streaming engine 125 of the exampleprocessor 100 of FIG. 1. FIG. 19 illustrates the processing of a singlestream representative of the two streams controlled by streaming engine125. Streaming engine 1900 includes stream address generator 1901.Stream address generator 1901 sequentially generates addresses of theelements of the stream and supplies these element addresses to systemmemory 1910. Memory 1910 recalls data stored at the element addresses(data elements) and supplies these data elements to datafirst-in-first-out (FIFO) buffer 1902. Data FIFO buffer 1902 providesbuffering between memory 1910 and processor 1920. Data formatter 1903receives the data elements from data FIFO memory 1902 and provides dataformatting according to the stream definition. This process is describedin more detail herein. Streaming engine 1900 supplies the formatted dataelements from data formatter 1903 to the processor 1920. A programexecuting on processor 1920 consumes the data and generates an output.

Stream elements typically reside in system memory. The memory imposes noparticular structure upon the stream. Programs define streams andthereby impose structure by specifying the stream attributes such asaddress of the first element of the stream, size and type of theelements in the stream, formatting for data in the stream, and theaddress sequence associated with the stream.

The streaming engine defines an address sequence for elements of thestream in terms of a pointer walking through memory. A multiple-levelnested loop controls the path the pointer takes. An iteration count fora loop level indicates the number of times the level repeats. Adimension gives the distance between pointer positions of the looplevel.

In a basic forward stream, the innermost loop consumes physicallycontiguous elements from memory as the implicit dimension of theinnermost loop is one element. The pointer moves from element to elementin consecutive, increasing order. In each level outside the inner loop,that loop moves the pointer to a new location based on the size of thedimension of the loop level. This form of addressing allows programs tospecify regular paths through memory using a small number of parameters.Table 4 lists the addressing parameters of a basic stream. In thisexample, ELEM_BYTES ranges from 1 to 64 bytes as shown in Table 5.

TABLE 4 Parameter Definition ELEM_ Size of each element in bytes BYTESICNT0 Number of iterations for the innermost loop level 0. At loop level0 all elements are physically contiguous. Implied DIM0 = ELEM_BYTESICNT1 Number of iterations for loop level 1 DIM1 Number of bytes betweenthe starting points for consecutive iterations of loop level 1 ICNT2Number of iterations for loop level 2 DIM2 Number of bytes between thestarting points for consecutive iterations of loop level 2 ICNT3 Numberof iterations for loop level 3 DIM3 Number of bytes between the startingpoints for consecutive iterations of loop level 3 ICNT4 Number ofiterations for loop level 4 DIM4 Number of bytes between the startingpoints for consecutive iterations of loop level 4 ICNT5 Number ofiterations for loop level 5 DIM5 Number of bytes between the startingpoints for consecutive iterations of loop level 5

TABLE 5 ELEM_BYTES Stream Element Length 000  1 byte 001  2 bytes 010  4bytes 011  8 bytes 100 16 bytes 101 32 bytes 110 64 bytes 111 Reserved

The definition above maps consecutive elements of the stream toincreasing addresses in memory which is appropriate for many algorithms.Some algorithms are better served by reading elements in decreasingmemory address order or reverse stream addressing. For example, adiscrete convolution computes vector dot-products as per

(f*g)[t]=Σ_(x=−∞) ^(∞) f[x]g[t−x]

where f[ ] and g[ ] represent arrays in memory. For each output, thealgorithm reads f[ ] in the forward direction and reads g[ ] in thereverse direction. Practical filters limit the range of indices for [x]and [t−x] to a finite number of elements. To support this pattern, thestreaming engine supports reading elements in decreasing address order.

Matrix multiplication presents a unique problem to the streaming engine.Each element in the matrix product is a vector dot product between a rowfrom the first matrix and a column from the second. Programs typicallystore matrices in row-major or column-major order. Row-major orderstores all the elements of a single row contiguously in memory.Column-major order stores all elements of a single column contiguouslyin memory. Matrices are typically stored in the same order as thedefault array order for the language. As a result, only one of the twomatrices in a matrix multiplication map on to the 2-dimensional streamdefinition of the streaming engine. In a typical example, an index stepsthrough columns on one array and rows of the other array. The streamingengine supports implicit matrix transposition with transposed streams.Transposed streams avoid the cost of explicitly transforming the data inmemory. Instead of accessing data in strictly consecutive-element order,the streaming engine effectively interchanges the inner two loopdimensions of the traversal order, fetching elements along the seconddimension into contiguous vector lanes.

This algorithm works but is impractical to implement for small elementsizes. Some algorithms work on matrix tiles which are multiple columnsand rows together. Therefore, the streaming engine defines a separatetransposition granularity. The hardware imposes a minimum granularity.The transpose granularity needs to be at least as large as the elementsize. Transposition granularity causes the streaming engine to fetch oneor more consecutive elements from dimension 0 before moving alongdimension 1. When the granularity equals the element size, a singlecolumn from a row-major array is fetched. Otherwise, the granularityspecifies fetching two, four or more columns at a time from a row-majorarray. This is also applicable for column-major layout by exchanging rowand column in the description. A parameter GRANULE indicates thetransposition granularity in bytes.

Another common matrix multiplication technique exchanges the innermosttwo loops of the matrix multiply. The resulting inner loop no longerreads down the column of one matrix while reading across the row ofanother. For example, the algorithm may hoist one term outside the innerloop, replacing it with the scalar value. The innermost loop can beimplemented with a single scalar by vector multiply followed by a vectoradd. Or, the scalar value can be duplicated across the length of thevector and a vector by vector multiply used. The streaming engine ofthis example directly supports the latter case and related use modelswith an element duplication mode. In this mode, the streaming enginereads a granule smaller than the full vector size and replicates thatgranule to fill the next vector output.

The streaming engine treats each complex number as a single element withtwo sub-elements that give the real and imaginary (rectangular) ormagnitude and angle (polar) portions of the complex number. Not allprograms or peripherals agree what order these sub-elements shouldappear in memory. Therefore, the streaming engine offers the ability toswap the two sub-elements of a complex number with no cost. The featureswaps the halves of an element without interpreting the contents of theelement and can be used to swap pairs of sub-elements of any type, notjust complex numbers.

Algorithms generally prefer to work at high precision, but highprecision values require more storage and bandwidth than lower precisionvalues. Commonly, programs store data in memory at low precision,promote those values to a higher precision for calculation, and thendemote the values to lower precision for storage. The streaming enginesupports such operations directly by allowing algorithms to specify onelevel of type promotion. In this example, every sub-element can bepromoted to a larger type size with either sign or zero extension forinteger types. In some examples, the streaming engine supports floatingpoint promotion, promoting 16-bit and 32-bit floating point values to32-bit and 64-bit formats, respectively.

While the streaming engine defines a stream as a discrete sequence ofdata elements, the processing unit core 110 consumes data elementspacked contiguously in vectors. The vectors resemble streams as thevectors contain multiple homogeneous elements with some implicitsequence. Because the streaming engine reads streams, but the processingunit core 110 consumes vectors, the streaming engine maps streams ontovectors in a consistent way.

Vectors are divided into equal-sized lanes, each lane allocated tostoring a sub-element. The processing unit core 110 designates therightmost lane of the vector as lane 0, regardless of current endianmode. Lane numbers increase right-to-left. The actual number of laneswithin a vector varies depending on the length of the vector and thedata size of the sub-element. Further, the lanes may be referred to aslanes, vector lanes, or SIMD lanes herein.

FIG. 20 illustrates the sequence of the formatting operations offormatter 1903. Formatter 1903 includes three sections: input section2010, formatting section 2020, and output section 2030. Input section2010 receives the data recalled from system memory 1910 as accessed bystream address generator 1901. The data can be via linear fetch stream2011 or transposed fetch stream 2012.

Formatting section 2020 includes various formatting blocks. Theformatting performed within formatter 1903 by the blocks is furtherdescribed below. Complex swap block 2021 optionally swaps twosub-elements forming a complex number element. Type promotion block 2022optionally promotes each data element into a larger data size. Promotionincludes zero extension for unsigned integers and sign extension forsigned integers. Decimation block 2023 optionally decimates the dataelements. In this example, decimation can be 2:1 retaining every otherdata element or 4:1 retaining every fourth data element. Elementduplication block 2024 optionally duplicates individual data elements.In this example, the data element duplication is an integer power of 2(2^(N), where N is an integer) including 2×, 4×, 8×, 16×, 32× and 64×.In this example, data duplication can extend over multiple destinationvectors. Vector length masking/group duplication block 2025 has twoprimary functions. An independently specified vector length VECLENcontrols the data elements supplied to each output data vector. Whengroup duplication is off, excess lanes in the output data vector arezero filled and these lanes are marked invalid. When group duplicationis on, input data elements of the specified vector length are duplicatedto fill the output data vector.

Output section 2030 holds the data for output to the correspondingfunctional units. Register and buffer for processor 2031 stores aformatted vector of data to be used as an operand by the functionalunits of processing unit core 110 (FIG. 1).

FIG. 21 illustrates an example of lane allocation in a vector. Vector2100 is divided into eight 64-bit lanes (8×64 bits=512 bits, the vectorlength). Lane 0 includes bits 0 to 63, lane 1 includes bits 64 to 127,lane 2 includes bits 128 to 191, lane 3 includes bits 192 to 255, lane 4includes bits 256 to 319, lane 5 includes bits 320 to 383, lane 6includes bits 384 to 447, and lane 7 includes bits 448 to 511.

FIG. 22 illustrates another example of lane allocation in a vector.Vector 2210 is divided into sixteen 32-bit lanes (16×32 bits=512 bits,the vector length). Lane 0 includes bits 0 to 31, lane 1 includes bits32 to 63, lane 2 includes bits 64 to 95, lane 3 includes bits 96 to 127,lane 4 includes bits 128 to 159, lane 5 includes bits 160 to 191, lane 6includes bits 192 to 223, lane 7 includes bits 224 to 255, lane 8includes bits 256 to 287, lane 9 includes bits 288 to 319, lane 10includes bits 320 to 351, lane 11 includes bits 352 to 383, lane 12includes bits 384 to 415, lane 13 includes bits 416 to 447, lane 14includes bits 448 to 479, and lane 15 includes bits 480 to 511.

The streaming engine maps the innermost stream dimension directly tovector lanes. The streaming engine maps earlier elements within theinnermost stream dimension to lower lane numbers and later elements tohigher lane numbers, regardless of whether the stream advances inincreasing or decreasing address order. Whatever order the streamdefines, the streaming engine deposits elements in vectors inincreasing-lane order. For non-complex data, the streaming engine placesthe first element in lane 0 of the vector processing unit core 110(FIG. 1) fetches, the second in lane 1, and so on. For complex data, thestreaming engine places the first element in lanes 0 and 1, the secondelement in lanes 2 and 3, and so on. Sub-elements within an elementretain the same relative ordering regardless of the stream direction.For non-swapped complex elements, the sub-elements with the loweraddress of each pair are placed in the even numbered lanes, and thesub-elements with the higher address of each pair are placed in the oddnumbered lanes. For swapped complex elements, the placement is reversed.

The streaming engine fills each vector processing unit core 110 fetcheswith as many elements as possible from the innermost stream dimension.If the innermost dimension is not a multiple of the vector length, thestreaming engine zero pads the dimension to a multiple of the vectorlength. As noted below, the streaming engine also marks the lanesinvalid. Thus, for higher-dimension streams, the first element from eachiteration of an outer dimension arrives in lane 0 of a vector. Thestreaming engine maps the innermost dimension to consecutive lanes in avector. For transposed streams, the innermost dimension includes groupsof sub-elements along dimension 1, not dimension 0, as transpositionexchanges these two dimensions.

Two-dimensional (2D) streams exhibit greater variety as compared toone-dimensional streams. A basic 2D stream extracts a smaller rectanglefrom a larger rectangle. A transposed 2D stream reads a rectanglecolumn-wise instead of row-wise. A looping stream, where the seconddimension overlaps first, executes a finite impulse response (FIR)filter taps which loops repeatedly over FIR filter samples providing asliding window of input samples.

FIG. 23 illustrates a region of memory that can be accessed using abasic two-dimensional stream. The inner two dimensions, represented byELEM_BYTES, ICNT0, DIM1 and ICNT1 (refer to Table 4), give sufficientflexibility to describe extracting a smaller rectangle 2320 havingdimensions 2321 and 2322 from a larger rectangle 2310 having dimensions2311 and 2312. In this example, rectangle 2320 is a 9 by 13 rectangle of64-bit values and rectangle 2310 is a larger 11 by 19 rectangle. Thefollowing stream parameters define this stream:

ICNT0=9, ELEM_BYTES=8, ICNT1=13, and DIM1=88 (11 times 8).

Thus, the iteration count in the 0-dimension 2321 is nine and theiteration count in the 1-dimension 2322 is thirteen. Note that theELEM_BYTES scales the innermost dimension. The first dimension has ICNT0elements of size ELEM_BYTES. The stream address generator does not scalethe outer dimensions. Therefore, DIM1=88, which is eleven elementsscaled by eight bytes per element.

FIG. 24 illustrates the order of elements within the example stream ofFIG. 23. The streaming engine fetches elements for the stream in theorder illustrated in order 2400. The first nine elements come from thefirst row of rectangle 2320, left-to-right in hops 1 to 8. The 10ththrough 24th elements comes from the second row, and so on. When thestream moves from the 9th element to the 10th element (hop 9 in FIG.24), the streaming engine computes the new location based on theposition of the pointer at the start of the inner loop, not the positionof the pointer at the end of the first dimension. Thus, DIM1 isindependent of ELEM_BYTES and ICNT0. DIM1 represents the distancebetween the first bytes of each consecutive row.

Transposed streams are accessed along dimension 1 before dimension 0.The following examples illustrate transposed streams with varyingtransposition granularity. FIG. 25 illustrates extracting a smallerrectangle 2520 (12×8) having dimensions 2521 and 2522 from a largerrectangle 2510 (14×13) having dimensions 2511 and 2512. In FIG. 25,ELEM_BYTES equal 2.

FIG. 26 illustrates how the streaming engine fetches the stream of theexample stream of FIG. 25 with a transposition granularity of fourbytes. Fetch pattern 2600 fetches pairs of elements from each row(because the granularity of four is twice the ELEM_BYTES of two), butotherwise moves down the columns. Once the streaming engine reaches thebottom of a pair of columns, the streaming engine repeats the patternwith the next pair of columns.

FIG. 27 illustrates how the streaming engine fetches the stream of theexample stream of FIG. 25 with a transposition granularity of eightbytes. The overall structure remains the same. The streaming enginefetches four elements from each row (because the granularity of eight isfour times the ELEM_BYTES of two) before moving to the next row in thecolumn as shown in fetch pattern 2700.

The streams examined so far read each element from memory exactly once.A stream can read a given element from memory multiple times, in effectlooping over a portion of memory. FIR filters exhibit two common loopingpatterns: re-reading the same filter taps for each output and readinginput samples from a sliding window. Two consecutive outputs need inputsfrom two overlapping windows.

FIG. 28 illustrates the details of streaming engine 125 of FIG. 1.Streaming engine 125 contains three major sections: Stream 0 2810;Stream 1 2820; and Shared L2 Interfaces 2830. Stream 0 2810 and Stream 12820 both contain identical hardware that operates in parallel. Stream 02810 and Stream 1 2820 both share L2 interfaces 2830. Each stream 2810and 2820 provides processing unit core 110 (FIG. 1) data at a rate of upto 512 bits/cycle, every cycle, which is enabled by the dedicated streampaths and shared dual L2 interfaces.

Each streaming engine 125 includes a respective dedicated 6-dimensional(6D) stream address generator 2811/2821 that can each generate one newnon-aligned request per cycle. As is further described herein, addressgenerators 2811/2821 output 512-bit aligned addresses that overlap theelements in the sequence defined by the stream parameters.

Each address generator 2811/2821 connects to a respective dedicatedmicro table look-aside buffer (μTLB) 2812/2822. The μTLB 2812/2822converts a single 48-bit virtual address to a 44-bit physical addresseach cycle. Each μTLB 2812/2822 has 8 entries, covering a minimum of 32kB with 4 kB pages or a maximum of 16 MB with 2 MB pages. Each addressgenerator 2811/2821 generates 2 addresses per cycle. The μTLB 2812/2822only translates one address per cycle. To maintain throughput, streamingengine 125 operates under the assumption that most stream references arewithin the same 4 kB page. Thus, the address translation does not modifybits 0 to 11 of the address. If aout0 and aout1 line in the same 4 kBpage (aout0[47:12] are the same aout1[47:12]), then the μTLB 2812/2822only translates aout0 and reuses the translation for the upper bits ofboth addresses.

Translated addresses are queued in respective command queue 2813/2823.These addresses are aligned with information from the respectivecorresponding Storage Allocation and Tracking block 2814/2824. Streamingengine 125 does not explicitly manage μTLB 2812/2822. The system memorymanagement unit (MMU) invalidates μTLBs as necessary during contextswitches.

Storage Allocation and Tracking 2814/2824 manages the internal storageof the stream, discovering data reuse and tracking the lifetime of eachpiece of data. The block accepts two virtual addresses per cycle andbinds those addresses to slots in the internal storage if the addressesare not already allocated to slots. The data store is organized as anarray of slots. The streaming engine maintains the following metadata totrack the contents and lifetime of the data in each slot: a 49-bitvirtual address associated with the slot, a valid bit indicating whetherthe tag address is valid, a ready bit indicating data has arrived forthe address, an active bit indicating if there are any referencesoutstanding to this data, and a last reference value indicating the mostrecent reference to this slot in the reference queue. The storageallocation and tracking are further described herein.

Respective reference queue 2815/2825 stores the sequence of referencesgenerated by the respective corresponding address generator 2811/2821.The reference sequence enables the data formatting network to presentdata to processing unit core 110 in the correct order. Each entry inrespective reference queue 2815/2825 contains the information necessaryto read data out of the data store and align the data for processingunit core 110. Respective reference queue 2815/2825 maintains theinformation listed in Table 6 in each slot.

TABLE 6 Data Slot Low Slot number for the lower half of data associatedwith aout0 Data Slot High Slot number for the upper half of dataassociated with aout1 Rotation Number of bytes to rotate data to alignnext element with lane 0 Length Number of valid bytes in this reference

Storage allocation and tracking 2814/2824 inserts references inreference queue 2815/2825 as address generator 2811/2821 generates newaddresses. Storage allocation and tracking 2814/2824 removes referencesfrom reference queue 2815/2825 when the data becomes available and thereis room in the stream head registers. As storage allocation and tracking2814/2824 removes slot references from reference queue 2815/2825 andformats data, the references are checked for the last reference to thecorresponding slots. Storage allocation and tracking 2814/2824 comparesreference queue 2815/2825 removal pointer against the recorded lastreference of the slot. If the pointer and the recorded last referencematch, then storage allocation and tracking 2814/2824 marks the slotinactive once the data is no longer needed.

Streaming engine 125 has respective data storage 2816/2826 for aselected number of elements. Deep buffering allows the streaming engineto fetch far ahead in the stream, hiding memory system latency. Eachdata storage 2816/2826 accommodates two simultaneous read operations andtwo simultaneous write operations per cycle and each is thereforereferred to a two-read, two-write (2r2w) data storage. In otherexamples, the amount of buffering can be different. In the currentexample, streaming engine 125 dedicates 32 slots to each stream witheach slot tagged by the previously described metadata. Each slot holds64 bytes of data in eight banks of eight bytes.

Data storage 2816/2826 and the respective storage allocation/trackinglogic 2814/2824 and reference queues 2815/2825 implement the data FIFO1902 discussed with reference to FIG. 19.

Respective butterfly network 2817/2827 includes a seven-stage butterflynetwork that implements the formatter 1903 (FIG. 19, FIG. 20). Butterflynetwork 2817/2827 receives 128 bytes of input and generates 64 bytes ofoutput. The first stage of the butterfly is actually a half-stage thatcollects bytes from both slots that match a non-aligned fetch and mergesthe collected bytes into a single, rotated 64-byte array. The remainingsix stages form a standard butterfly network. Respective butterflynetwork 2817/2827 performs the following operations: rotates the nextelement down to byte lane 0; promotes data types by a power of two, ifrequested; swaps real and imaginary components of complex numbers, ifrequested; and converts big endian to little endian if processing unitcore 110 is presently in big endian mode. The user specifies elementsize, type promotion, and real/imaginary swap as part of the parametersof the stream.

Streaming engine 125 attempts to fetch and format data ahead ofprocessing unit core 110's demand in order to maintain full throughput.Respective stream head registers 2818/2828 provide a small amount ofbuffering so that the process remains fully pipelined. Respective streamhead registers 2818/2828 are not directly architecturally visible. Eachstream also has a respective stream valid register 2819/2829. Validregisters 2819/2829 indicate which elements in the corresponding streamhead registers 2818/2828 are valid.

The two streams 2810/2820 share a pair of independent L2 interfaces2830: L2 Interface A (IFA) 2833 and L2 Interface B (IFB) 2834. Each L2interface provides 512 bits/cycle throughput direct to the L2 controller130 (FIG. 1) via respective buses 147/149 for an aggregate bandwidth of1024 bits/cycle. The L2 interfaces use the credit-based multicore busarchitecture (MBA) protocol. The MBA protocol is described in moredetail in U.S. Pat. No. 9,904,645, “Multicore Bus Architecture withNon-Blocking High Performance Transaction Credit System,” which isincorporated by reference herein. The L2 controller assigns a pool ofcommand credits to each interface. The pool has sufficient credits sothat each interface can send sufficient requests to achieve fullread-return bandwidth when reading L2 RAM, L2 cache and multicore sharedmemory controller (MSMC) memory, as described in more detail herein.

To maximize performance, in this example both streams can use both L2interfaces, allowing a single stream to send a peak command rate of tworequests per cycle. Each interface prefers one stream over the other,but this preference changes dynamically from request to request. IFA2833 and IFB 2834 prefer opposite streams, when IFA 2833 prefers Stream0, IFB 2834 prefers Stream 1 and vice versa.

Respective arbiter 2831/2832 ahead of each respective interface2833/2834 applies the following basic protocol on every cycle havingcredits available. Arbiter 2831/2832 checks if the preferred stream hasa command ready to send. If so, arbiter 2831/2832 chooses that command.Arbiter 2831/2832 next checks if an alternate stream has at least tworequests ready to send, or one command and no credits. If so, arbiter2831/2832 pulls a command from the alternate stream. If either interfaceissues a command, the notion of preferred and alternate streams swap forthe next request. Using this algorithm, the two interfaces dispatchrequests as quickly as possible while retaining fairness between the twostreams. The first rule ensures that each stream can send a request onevery cycle that has available credits. The second rule provides amechanism for one stream to borrow the interface of the other when thesecond interface is idle. The third rule spreads the bandwidth demandfor each stream across both interfaces, ensuring neither interfacebecomes a bottleneck.

Respective coarse grain rotator 2835/2836 enables streaming engine 125to support a transposed matrix addressing mode. In this mode, streamingengine 125 interchanges the two innermost dimensions of themultidimensional loop to access an array column-wise rather thanrow-wise. Respective rotators 2835/2836 are not architecturally visible.

FIG. 29 illustrates an example stream template register 2900. The streamdefinition template provides the full structure of a stream thatcontains data. The iteration counts and dimensions provide most of thestructure, while the various flags provide the rest of the details. Inthis example, a single stream template 2900 is defined for alldata-containing streams. All stream types supported by the streamingengine are covered by the template 2900. The streaming engine supports asix-level loop nest for addressing elements within the stream. Most ofthe fields in the stream template 2900 map directly to the parameters inthat algorithm. The numbers above the fields are bit numbers within a256-bit vector. Table 7 shows the stream field definitions of a streamtemplate.

TABLE 7 FIG. 29 Field Reference Size Name Number Description Bits ICNT02901 Iteration count for loop 0 32 ICNT1 2902 Iteration count for loop 132 ICNT2 2903 Iteration count for loop 2 32 ICNT3 2904 Iteration countfor loop 3 32 ICNT4 2905 Iteration count for loop 4 32 ICNT5 2906Iteration count for loop 5 32 DIM1 2911 Signed dimension for loop 1 32DIM2 2912 Signed dimension for loop 2 32 DIM3 2913 Signed dimension forloop 3 32 DIM4 2914 Signed dimension for loop 4 32 DIM5 2915 Signeddimension for loop 5 32 FLAGS 2921 Stream modifier flags 64

Loop 0 is the innermost loop and loop 5 is the outermost loop. In thecurrent example, DIM0 is equal to ELEM_BYTES defining physicallycontiguous data. Thus, the stream template register 2900 does not defineDIM0. Streaming engine 125 interprets iteration counts as unsignedintegers and dimensions as unscaled signed integers. An iteration countof zero at any level (ICNT0, ICNT1, ICNT2, ICNT3, ICNT4 or ICNT5)indicates an empty stream. Each iteration count must be at least one todefine a valid stream. The template above specifies the type ofelements, length and dimensions of the stream. The stream instructionsseparately specify a start address, e.g., by specification of a scalarregister in scalar register file 211 which stores the start address.Thus, a program can open multiple streams using the same template butdifferent registers storing the start address.

FIG. 30 illustrates an example of sub-field definitions of the flagsfield 2921 shown in FIG. 29. As shown in FIG. 30, the flags field 2911is 6 bytes or 48 bits. FIG. 30 shows bit numbers of the fields. Table 8shows the definition of these fields.

TABLE 8 FIG. 30 Reference Size Field Name Number Description Bits ELTYPE3001 Type of data element 4 TRANSPOSE 3002 Two-dimensional transposemode 3 PROMOTE 3003 Promotion mode 3 VECLEN 3004 Stream vector length 3ELDUP 3005 Element duplication 3 GRDUP 3006 Group duplication 1 DECIM3007 Element decimation 2 THROTTLE 3008 Fetch ahead throttle mode 2DIMFMT 3009 Stream dimensions format 3 DIR 3010 Stream direction 1 0forward direction 1 reverse direction CBK0 3011 First circular blocksize number 4 CBK1 3012 Second circular block size number 4 AM0 3013Addressing mode for loop 0 2 AM1 3014 Addressing mode for loop 1 2 AM23015 Addressing mode for loop 2 2 AM3 3016 Addressing mode for loop 3 2AM4 3017 Addressing mode for loop 4 2 AM5 3018 Addressing mode for loop5 2

The Element Type (ELTYPE) field 3001 defines the data type of theelements in the stream. The coding of the four bits of the ELTYPE field3001 is defined as shown in Table 9.

TABLE 9 Sub-element Total Element ELTYPE Real/Complex Size Bits SizeBits 0000 real 8 8 0001 real 16 16 0010 real 32 32 0011 real 64 64 0100reserved 0101 reserved 0110 reserved 0111 reserved 1000 complex 8 16 noswap 1001 complex 16 32 no swap 1010 complex 32 64 no swap 1011 complex64 128 no swap 1100 complex 8 16 swapped 1101 complex 16 32 swapped 1110complex 32 64 swapped 1111 complex 64 128 swapped

Real/Complex Type determines whether the streaming engine treats eachelement as a real number or two parts (real/imaginary ormagnitude/angle) of a complex number and also specifies whether to swapthe two parts of complex numbers. Complex types have a total elementsize twice the sub-element size. Otherwise, the sub-element size equalsthe total element size.

Sub-Element Size determines the type for purposes of type promotion andvector lane width. For example, 16-bit sub-elements get promoted to32-bit sub-elements or 64-bit sub-elements when a stream requests typepromotion. The vector lane width matters when processing unit core 110(FIG. 1) operates in big endian mode, as the core 110 lays out vectorsin little endian order.

Total Element Size specifies the minimal granularity of the stream whichdetermines the number of bytes the stream fetches for each iteration ofthe innermost loop. Streams read whole elements, either in increasing ordecreasing order. Therefore, the innermost dimension of a stream spansICNT0×total-element-size bytes.

The TRANSPOSE field 3002 determines whether the streaming engineaccesses the stream in a transposed order. The transposed orderexchanges the inner two addressing levels. The TRANSPOSE field 3002 alsoindicated the granularity for transposing the stream. The coding of thethree bits of the TRANSPOSE field 3002 is defined as shown in Table 10for normal 2D operations.

TABLE 10 Transpose Meaning 000 Transpose disabled 001 Transpose on 8-bitboundaries 010 Transpose on 16-bit boundaries 011 Transpose on 32-bitboundaries 100 Transpose on 64-bit boundaries 101 Transpose on 128-bitboundaries 110 Transpose on 256-bit boundaries 111 Reserved

Streaming engine 125 can transpose data elements at a differentgranularity than the element size thus allowing programs to fetchmultiple columns of elements from each row. The transpose granularitycannot be smaller than the element size. The TRANSPOSE field 3002interacts with the DIMFMT field 3009 in a manner further describedbelow.

The PROMOTE field 3003 controls whether the streaming engine promotessub-elements in the stream and the type of promotion. When enabled,streaming engine 125 promotes types by powers-of-2 sizes. The coding ofthe three bits of the PROMOTE field 3003 is defined as shown in Table11.

TABLE 11 Promotion Promotion Resulting Sub-element Size PROMOTE FactorType  8-bit 16-bit 32-bit 64-bit 000 1x N/A  8-bit 16-bit 32-bit 64-bit001 2x zero extend 16-bit 32-bit 64-bit Invalid 010 4x zero extend32-bit 64-bit Invalid Invalid 011 8x zero extend 64-bit Invalid InvalidInvalid 100 reserved 101 2x sign extend 16-bit 32-bit 64-bit Invalid 1104x sign extend 32-bit 64-bit Invalid Invalid 111 8x sign extend 64-bitInvalid Invalid Invalid

When PROMOTE is 000, corresponding to a 1× promotion, each sub-elementis unchanged and occupies a vector lane equal in width to the sizespecified by ELTYPE. When PROMOTE is 001, corresponding to a 2×promotion and zero extend, each sub-element is treated as an unsignedinteger and zero extended to a vector lane twice the width specified byELTYPE. A 2× promotion is invalid for an initial sub-element size of 64bits. When PROMOTE is 010, corresponding to a 4× promotion and zeroextend, each sub-element is treated as an unsigned integer and zeroextended to a vector lane four times the width specified by ELTYPE. A 4×promotion is invalid for an initial sub-element size of 32 or 64 bits.When PROMOTE is 011, corresponding to an 8× promotion and zero extend,each sub-element is treated as an unsigned integer and zero extended toa vector lane eight times the width specified by ELTYPE. An 8× promotionis invalid for an initial sub-element size of 16, 32 or 64 bits. WhenPROMOTE is 101, corresponding to a 2× promotion and sign extend, eachsub-element is treated as a signed integer and sign extended to a vectorlane twice the width specified by ELTYPE. A 2× promotion is invalid foran initial sub-element size of 64 bits. When PROMOTE is 110,corresponding to a 4× promotion and sign extend, each sub-element istreated as a signed integer and sign extended to a vector lane fourtimes the width specified by ELTYPE. A 4× promotion is invalid for aninitial sub-element size of 32 or 64 bits. When PROMOTE is 111,corresponding to an 8× promotion and zero extend, each sub-element istreated as a signed integer and sign extended to a vector lane eighttimes the width specified by ELTYPE. An 8× promotion is invalid for aninitial sub-element size of 16, 32 or 64 bits.

The VECLEN field 3004 defines the stream vector length for the stream inbytes. Streaming engine 125 breaks the stream into groups of elementsthat are VECLEN bytes long. The coding of the three bits of the VECLENfield 3004 is defined as shown in Table 12.

TABLE 12 VECLEN Stream Vector Length 000  1 byte 001  2 bytes 010  4bytes 011  8 bytes 100 16 bytes 101 32 bytes 110 64 bytes 111 Reserved

VECLEN cannot be less than the product of the element size in bytes andthe duplication factor. As shown in Table 11, the maximum VECLEN of 64bytes equals the preferred vector size of vector data path side B 116.When VECLEN is shorter than the native vector width of processing unitcore 110, streaming engine 125 pads the extra lanes in the vectorprovided to processing unit core 110. The GRDUP field 3006 determinesthe type of padding. The VECLEN field 3004 interacts with ELDUP field3005 and GRDUP field 3006 in a manner detailed below.

The ELDUP field 3005 specifies the number of times to duplicate eachelement. The element size multiplied with the element duplication amountcannot exceed the 64 bytes. The coding of the three bits of the ELDUPfield 3005 is defined as shown in Table 13.

TABLE 13 ELDUP Duplication Factor 000 No Duplication 001  2 times 010  4times 011  8 times 100 16 times 101 32 times 110 64 times 111 Reserved

The ELDUP field 3005 interacts with VECLEN field 3004 and GRDUP field3006 in a manner detailed below. The nature of the relationship betweenthe permitted element size, the element duplication factor, and thedestination vector length requires that a duplicated element thatoverflows the first destination register fills an integer number ofdestination registers upon completion of duplication. The data of theadditional destination registers eventually supplies the respectivestream head register 2818/2828. Upon completion of duplication of afirst data element, the next data element is rotated down to the leastsignificant bits of source register 3100 discarding the first dataelement. The process then repeats for the new data element.

The GRDUP bit 3006 determines whether group duplication is enabled. IfGRDUP bit 3006 is 0, then group duplication is disabled. If the GRDUPbit 3006 is 1, then group duplication is enabled. When enabled by GRDUPbit 3006, streaming engine 125 duplicates a group of elements to fillthe vector width. VECLEN field 3004 defines the length of the group toreplicate. When VECLEN field 3004 is less than the vector length ofprocessing unit core 110 and GRDUP bit 3006 enables group duplication,streaming engine 125 fills the extra lanes (see FIGS. 21 and 22) withadditional copies of the stream vector. Because stream vector length andvector length of processing unit core 110 are integer powers of two,group duplication produces an integer number of duplicate copies. NoteGRDUP and VECLEN do not specify the number of duplications. The numberof duplications performed is based upon the ratio of VECLEN to thenative vector length, which is 64 bytes/512 bits in this example.

The GRDUP field 3006 specifies how streaming engine 125 pads streamvectors for bits following the VECLEN length to the vector length ofprocessing unit core 110. When GRDUP bit 3006 is 0, streaming engine 125fills the extra lanes with zeros and marks the extra vector lanesinvalid. When GRDUP bit 3006 is 1, streaming engine 125 fills extralanes with copies of the group of elements in each stream vector.Setting GRDUP bit 3006 to 1 has no effect when VECLEN is set to thenative vector width of processing unit core 110. VECLEN must be at leastas large as the product of ELEM_BYTES and the element duplication factorELDUP. That is, an element or the duplication factor number of elementscannot be separated using VECLEN.

Group duplication operates to the destination vector size. Groupduplication does not change the data supplied when the product of theelement size ELEM_BYTES and element duplication factor ELDUP equals orexceeds the destination vector width. Under such conditions, the statesof the GRDUP bit 3006 and the VECLEN field 3004 have no effect on thesupplied data.

The set of examples below illustrate the interaction between VECLEN andGRDUP. Each of the following examples show how the streaming engine mapsa stream onto vectors across different stream vector lengths and thevector size of vector data path side B 116. The stream of this exampleincludes twenty-nine elements (E0 to E28) of 64 bits/8 bytes. The streamcan be a linear stream of twenty-nine elements or an inner loop of 29elements. The tables illustrate eight byte lanes such as shown in FIG.21. Each illustrated vector is stored in the respective stream headregister 2818/2828 in turn.

Table 14 illustrates how the example stream maps onto bits within the64-byte processor vectors when VECLEN is 64 bytes. As shown in Table 14,the stream extends over four vectors. As previously described, the laneswithin vector 4 that extend beyond the stream are zero filled. WhenVECLEN has a size equal to the native vector length, the value of GRDUPdoes not matter as no duplication can take place with such a VECLEN.

TABLE 14 Processor Lane Lane Lane Lane Lane Lane Lane Lane Vectors 7 6 54 3 2 1 0 1 E7 E6 E5 E4 E3 E2 E1 E0 2 E15 E14 E13 E12 E11 E10 E9 E8 3E23 E22 E21 E20 E19 E18 E17 E16 4 0 0 0 E28 E27 E26 E25 E24

Table 15 shows the same parameters as shown in Table 14, except withVECLEN of 32 bytes. Group duplicate is disabled (GRDUP=0). Thetwenty-nine elements of the stream are distributed over lanes 0 to 3 ineight vectors. Extra lanes 4 to 7 in vectors 1-7 are zero filled. Invector 8, lane 1 has a stream element (E28) and the other lanes are zerofilled.

TABLE 15 Processor Lane Lane Lane Lane Lane Lane Lane Lane Vectors 7 6 54 3 2 1 0 1 0 0 0 0 E3 E2 E1 E0 2 0 0 0 0 E7 E6 E5 E4 3 0 0 0 0 E11 E10E9 E8 4 0 0 0 0 E15 E14 E13 E12 5 0 0 0 0 E19 E18 E17 E16 6 0 0 0 0 E23E22 E21 E20 7 0 0 0 0 E27 E26 E25 E24 8 0 0 0 0 0 0 0 E28

Table 16 shows the same parameters as shown in Table 14, except withVECLEN of sixteen bytes. Group duplicate is disabled (GRDUP=0). Thetwenty-nine elements of the stream are distributed over lane 0 and lane1 in fifteen vectors. Extra lanes 2 to 7 in vectors 1-14 are zerofilled. In vector 15, lane 1 has a stream element (E28) and the otherlanes are zero filled.

TABLE 16 Processor Lane Lane Lane Lane Lane Lane Lane Lane Vectors 7 6 54 3 2 1 0  1 0 0 0 0 0 0 E1 E0  2 0 0 0 0 0 0 E3 E2  3 0 0 0 0 0 0 E5 E4 4 0 0 0 0 0 0 E7 E6  5 0 0 0 0 0 0 E9 E8  6 0 0 0 0 0 0 E11 E10  7 0 00 0 0 0 E13 E12  8 0 0 0 0 0 0 E15 E14  9 0 0 0 0 0 0 E17 E16 10 0 0 0 00 0 E19 E18 11 0 0 0 0 0 0 E21 E20 12 0 0 0 0 0 0 E23 E22 13 0 0 0 0 0 0E25 E24 14 0 0 0 0 0 0 E27 E26 15 0 0 0 0 0 0 0 E28

Table 17 shows the same parameters as shown in Table 14, except withVECLEN of eight bytes. Group duplicate is disabled (GRDUP=0). Thetwenty-nine elements of the stream appear in lane 0 in twenty-ninevectors. Extra lanes 1-7 in vectors 1-29 are zero filled.

TABLE 17 Processor Lane Lane Lane Lane Lane Lane Lane Lane Vectors 7 6 54 3 2 1 0  1 0 0 0 0 0 0 0 E0  2 0 0 0 0 0 0 0 E1  3 0 0 0 0 0 0 0 E2  40 0 0 0 0 0 0 E3  5 0 0 0 0 0 0 0 E4  6 0 0 0 0 0 0 0 E5  7 0 0 0 0 0 00 E6  8 0 0 0 0 0 0 0 E7  9 0 0 0 0 0 0 0 E8 10 0 0 0 0 0 0 0 E9 11 0 00 0 0 0 0 E10 12 0 0 0 0 0 0 0 E11 13 0 0 0 0 0 0 0 E12 14 0 0 0 0 0 0 0E13 15 0 0 0 0 0 0 0 E14 16 0 0 0 0 0 0 0 E15 17 0 0 0 0 0 0 0 E16 18 00 0 0 0 0 0 E17 19 0 0 0 0 0 0 0 E18 20 0 0 0 0 0 0 0 E19 21 0 0 0 0 0 00 E20 22 0 0 0 0 0 0 0 E21 23 0 0 0 0 0 0 0 E22 24 0 0 0 0 0 0 0 E23 250 0 0 0 0 0 0 E24 26 0 0 0 0 0 0 0 E25 27 0 0 0 0 0 0 0 E26 28 0 0 0 0 00 0 E27 29 0 0 0 0 0 0 0 E28

Table 18 shows the same parameters as shown in Table 15, except withVECLEN of thirty-two bytes and group duplicate is enabled (GRDUP=1). Thetwenty-nine elements of the stream are distributed over lanes 0-7 ineight vectors. Each vector 1-7 includes four elements duplicated. Theduplication factor (2) results because VECLEN (32 bytes) is half thenative vector length of 64 bytes. In vector 8, lane 0 has a streamelement (E28) and lanes 1-3 are zero filled. Lanes 4-7 of vector 9duplicate this pattern.

TABLE 18 Processor Lane Lane Lane Lane Lane Lane Lane Lane Vectors 7 6 54 3 2 1 0 1 E3 E2 E1 E0 E3 E2 E1 E0 2 E7 E6 E5 E4 E7 E6 E5 E4 3 E11 E10E9 E8 E11 E10 E9 E8 4 E15 E14 E13 E12 E15 E14 E13 E12 5 E19 E18 E17 E16E19 E18 E17 E16 6 E23 E22 E21 E20 E23 E22 E21 E20 7 E27 E26 E25 E24 E27E26 E25 E24 8 0 0 0 E28 0 0 0 E28

Table 19 shows the same parameters as shown in Table 16, except withVECLEN of sixteen bytes. Group duplicate is enabled (GRDUP=1). Thetwenty-nine elements of the stream are distributed over lanes 0-7 infifteen vectors. Each vector 1-7 includes two elements duplicated fourtimes. The duplication factor (4) results because VECLEN (16 bytes) isone quarter the native vector length of 64 bytes. In vector 15, lane 0has a stream element (E28) and lane 1 is zero filled. This pattern isduplicated in lanes 2 and 3, lanes 4 and 5, and lanes 6 and 7 of vector15.

TABLE 19 Processor Lane Lane Lane Lane Lane Lane Lane Lane Vectors 7 6 54 3 2 1 0  1 E1 E0 E1 E0 E1 E0 E1 E0  2 E3 E2 E3 E2 E3 E2 E3 E2  3 E5 E4E5 E4 E5 E4 E5 E4  4 E7 E6 E7 E6 E7 E6 E7 E6  5 E9 E8 E9 E8 E9 E8 E9 E8 6 E11 E10 E11 E10 E11 E10 E11 E10  7 E13 E12 E13 E12 E13 E12 E13 E12  8E15 E14 E15 E14 E15 E14 E15 E14  9 E17 E16 E17 E16 E17 E16 E17 E16 10E19 E18 E19 E18 E19 E18 E19 E18 11 E21 E20 E21 E20 E21 E20 E21 E20 12E23 E22 E23 E22 E23 E22 E23 E22 13 E25 E24 E25 E24 E25 E24 E25 E24 14E27 E26 E27 E26 E27 E26 E27 E26 15 0 E28 0 E28 0 E28 0 E28

Table 20 shows the same parameters as shown in Table 17, except withVECLEN of eight bytes. Group duplicate is enabled (GRDUP=1). Thetwenty-nine elements of the stream all appear on lanes 0 to 7 intwenty-nine vectors. Each vector includes one element duplicated eighttimes. The duplication factor (8) results because VECLEN (8 bytes) isone eighth the native vector length of 64 bytes. Thus, each lane is thesame in vectors 1-29.

TABLE 20 Processor Lane Lane Lane Lane Lane Lane Lane Lane Vectors 7 6 54 3 2 1 0  1 E0 E0 E0 E0 E0 E0 E0 E0  2 E1 E1 E1 E1 E1 E1 E1 E1  3 E2 E2E2 E2 E2 E2 E2 E2  4 E3 E3 E3 E3 E3 E3 E3 E3  5 E4 E4 E4 E4 E4 E4 E4 E4 6 E5 E5 E5 E5 E5 E5 E5 E5  7 E6 E6 E6 E6 E6 E6 E6 E6  8 E7 E7 E7 E7 E7E7 E7 E7  9 E8 E8 E8 E8 E8 E8 E8 E8 10 E9 E9 E9 E9 E9 E9 E9 E9 11 E10E10 E10 E10 E10 E10 E10 E10 12 E11 E11 E11 E11 E11 E11 E11 E11 13 E12E12 E12 E12 E12 E12 E12 E12 14 E13 E13 E13 E13 E13 E13 E13 E13 15 E14E14 E14 E14 E14 E14 E14 E14 16 E15 E15 E15 E15 E15 E15 E15 E15 17 E16E16 E16 E16 E16 E16 E16 E16 18 E17 E17 E17 E17 E17 E17 E17 E17 19 E18E18 E18 E18 E18 E18 E18 E18 20 E19 E19 E19 E19 E19 E19 E19 E19 21 E20E20 E20 E20 E20 E20 E20 E20 22 E21 E21 E21 E21 E21 E21 E21 E21 23 E22E22 E22 E22 E22 E22 E22 E22 24 E23 E23 E23 E23 E23 E23 E23 E23 25 E24E24 E24 E24 E24 E24 E24 E24 26 E25 E25 E25 E25 E25 E25 E25 E25 27 E26E26 E26 E26 E26 E26 E26 E26 28 E27 E27 E27 E27 E27 E27 E27 E27 29 E28E28 E28 E28 E28 E28 E28 E28

FIG. 31 illustrates an example of vector length masking/groupduplication block 2025 (see FIG. 20) that is included within formatterblock 1903 of FIG. 19. Input register 3100 receives a vector input fromelement duplication block 2024 shown in FIG. 20. Input register 3100includes 64 bytes arranged in 64 1-byte blocks byte0 to byte63. Notethat bytes byte0 to byte63 are each equal in length to the minimum ofELEM_BYTES. A set of multiplexers 3101 to 3163 couple input bytes fromsource register 3100 to output register 3170. Each respectivemultiplexer 3101 to 3163 supplies an input to a respective byte1 tobyte63 of output register 3170. Not all input bytes byte0 to byte63 ofinput register 3100 are coupled to every multiplexer 3101 to 3163. Notethere is no multiplexer supplying byte0 of output register 3170. In thisexample, byte0 of output register 3170 is supplied by byte0 of inputregister 3100.

Multiplexers 3101 to 3163 are controlled by multiplexer control encoder3180. Multiplexer control encoder 3180 receives ELEM_BYTES, VECLEN andGRDUP input signals and generates respective control signals formultiplexers 3101 to 3163. ELEM_BYTES and ELDUP are supplied tomultiplexer control encoder 3180 to check to see that VECLEN is at leastas great as the product of ELEM_BYTES and ELDUP. In operation,multiplexer control encoder 3180 controls multiplexers 3101 to 3163 totransfer least significant bits equal in number to VECLEN from inputregister 3100 to output register 3170. If GRDUP=0 indicating groupduplication disabled, then multiplexer control encoder 3180 controls theremaining multiplexers 3101 to 3163 to transfer zeros to all bits in theremaining most significant lanes of output register 3170. If GRDUP=1indicating group duplication enabled, then multiplexer control encoder3180 controls the remaining multiplexers 3101 to 3163 to duplicate theVECLEN number of least significant bits of input register 3100 into themost significant lanes of output register 3170. This control is similarto the element duplication control described above and fills the outputregister 3170 with the first vector. For the next vector, data withininput register 3100 is rotated down by VECLEN, discarding the previousVECLEN least significant bits. The rate of data movement in formatter1903 (FIG. 19) is set by the rate of consumption of data by processingunit core 110 (FIG. 1) via stream read and advance instructionsdescribed below. The group duplication formatting repeats as long as thestream includes additional data elements.

Element duplication (ELDUP) and group duplication (GRDUP) areindependent. Note these features include independent specification andparameter setting. Thus, element duplication and group duplication canbe used together or separately. Because of how these are specified,element duplication permits overflow to the next vector while groupduplication does not.

Referring again to FIG. 30, the DECIM field 3007 controls data elementdecimation of the corresponding stream. Streaming engine 125 deletesdata elements from the stream upon storage in respective stream headregisters 2818/2828 for presentation to the requesting functional unit.Decimation removes whole data elements, not sub-elements. The DECIMfield 3007 is defined as listed in Table 21.

TABLE 21 DECIM Decimation Factor 00 No Decimation 01 2 times 10 4 times11 Reserved

If DECIM field 3007 equals 00, then no decimation occurs. The dataelements are passed to the corresponding stream head registers 2818/2828without change. If DECIM field 3007 equals 01, then 2:1 decimationoccurs. Streaming engine 125 removes odd number elements from the datastream upon storage in the stream head registers 2818/2828. Limitationsin the formatting network require 2:1 decimation to be employed withdata promotion by at least 2× (PROMOTE cannot be 000), ICNT0 must bemultiple of 2, and the total vector length (VECLEN) must be large enoughto hold a single promoted, duplicated element. For transposed streams(TRANSPOSE #0), the transpose granule must be at least twice the elementsize in bytes before promotion. If DECIM field 3007 equals 10, then 4:1decimation occurs. Streaming engine 125 retains every fourth dataelement removing three elements from the data stream upon storage in thestream head registers 2818/2828. Limitations in the formatting networkrequire 4:1 decimation to be employed with data promotion by at least 4×(PROMOTE cannot be 000, 001 or 101), ICNT0 must be a multiple of 4 andthe total vector length (VECLEN) must be large enough to hold a singlepromoted, duplicated element. For transposed streams (TRANSPOSE #0), inone example, decimation removes columns, and does not remove rows. Thus,in such cases, the transpose granule must be at least twice the elementsize in bytes before promotion for 2:1 decimation (GRANULE≥2×ELEM_BYTES)and at least four times the element size in bytes before promotion for4:1 decimation (GRANULE≥4×ELEM_BYTES).

The THROTTLE field 3008 controls how aggressively the streaming enginefetches ahead of processing unit core 110. The coding of the two bits ofthis field is defined as shown in Table 22.

TABLE 22 THROTTLE Description 00 Minimum throttling, maximum fetch ahead01 Less throttling, more fetch ahead 10 More throttling, less fetchahead 11 Maximum throttling, minimum fetch ahead

THROTTLE does not change the meaning of the stream and serves only as ahint. The streaming engine can ignore this field. Programs should notrely on the specific throttle behavior for program correctness, becausethe architecture does not specify the precise throttle behavior.THROTTLE allows programmers to provide hints to the hardware about theprogram behavior. By default, the streaming engine attempts to get asfar ahead of processing unit core 110 as possible to hide as muchlatency as possible (equivalent to THROTTLE=11), while providing fullstream throughput to processing unit core 110. While some applicationsneed this level of throughput, such throughput can cause bad systemlevel behavior for others. For example, the streaming engine discardsall fetched data across context switches. Therefore, aggressivefetch-ahead can lead to wasted bandwidth in a system with large numbersof context switches.

The DIMFMT field 3009 defines which of the loop count fields ICNT0 2901,ICNT1 2902, ICNT2 2903, ICNT3 2804, ICNT4 2905 and ICNT5 2906, of theloop dimension fields DIM1 2911, DIM2 2912, DIM3 2913, DIM4 2914 andDIM5 2915 and of the addressing mode fields AM0 3013, AM1 3014, AM23015, AM3 3016, AM4 3017 and AM5 3018 (part of FLAGS field 2921) of thestream template register 2900 are active for the particular stream.Table 23 lists the active loops for various values of the DIMFMT field3009. Each active loop count must be at least 1 and the outer activeloop count must be greater than 1.

TABLE 23 DIMFMT Loop5 Loop4 Loop3 Loop2 Loop1 Loop0 000 InactiveInactive Inactive Inactive Inactive Active 001 Inactive InactiveInactive Inactive Active Active 010 Inactive Inactive Inactive ActiveActive Active 011 Inactive Inactive Active Active Active Active 100Inactive Active Active Active Active Active 101 Active Active ActiveActive Active Active 110-111 Reserved

The DIR bit 3010 determines the direction of fetch of the inner loop(Loop0). If the DIR bit 3010 is 0, Loop0 fetches are in the forwarddirection toward increasing addresses. If the DIR bit 3010 is 1, Loop0fetches are in the backward direction toward decreasing addresses. Thefetch direction of other loops is determined by the sign of thecorresponding loop dimension DIM1, DIM2, DIM3, DIM4 and DIM5.

The CBK0 field 3011 and the CBK1 field 3012 control the circular blocksize upon selection of circular addressing. The manner of determiningthe circular block size is described herein.

The AM0 field 3013, AM1 field 3014, AM2 field 3015, AM3 field 3016, AM4field 3017 and AM5 field 3018 control the addressing mode of acorresponding loop, thus permitting the addressing mode to beindependently specified for each loop. Each of AM0 field 3013, AM1 field3014, AM2 field 3015, AM3 field 3016, AM4 field 3017 and AM5 field 3018are three bits and are decoded as listed in Table 24.

TABLE 24 AMx field Meaning 00 Linear addressing 01 Circular addressingblock size set by CBK0 10 Circular addressing block size set by CBK0 +CBK1 + 1 11 reserved

In linear addressing, the address advances according to the addressarithmetic whether forward or reverse. In circular addressing, theaddress remains within a defined address block. Upon reaching the end ofthe circular address block the address wraps around to the beginninglimit of the block. Circular addressing blocks are limited to 2Naddresses where N is an integer. Circular address arithmetic can operateby cutting the carry chain between bits and not allowing a selectednumber of most significant bits to change. Thus, arithmetic beyond theend of the circular block changes only the least significant bits. Theblock size is set as listed in Table 25.

TABLE 25 Encoded Block Size CBK0 or CBK0 + CBK1 + 1 Block Size (bytes) 0512 1  1K 2  2K 3  4K 4  8K 5  16K 6  32K 7  64K 8 128K 9 256K 10 512K11  1M 12  2M 13  4M 14  8M 15  16M 16  32M 17  64M 18 128M 19 256M 20512M 21  1 G 22  2 G 23  4 G 24  8 G 25  16 G 26  32 G 27  64 G 28Reserved 29 Reserved 30 Reserved 31 Reserved

In this example, the circular block size is set by the number encoded byCBK0 (first circular address mode 01) or the number encoded byCBK0+CBK1+1 (second circular address mode 10). For example, in the firstcircular address mode, the circular address block size can range from512 bytes to 16 M bytes. For the second circular address mode, thecircular address block size can range from 1 K bytes to 64 G bytes.Thus, the encoded block size is 2^((B+9)) bytes, where B is the encodedblock number which is CBK0 for the first block size (AMx of 01) andCBK0+CBK1+1 for the second block size (AMx of 10).

The processing unit 110 (FIG. 1) exposes the streaming engine 125 (FIG.28) to programs through a small number of instructions and specializedregisters. Programs start and end streams with SEOPEN and SECLOSE.SEOPEN opens a new stream and the stream remains open until terminatedexplicitly by SECLOSE or replaced by a new stream with SEOPEN. TheSEOPEN instruction specifies a stream number indicating opening stream 0or stream 1. The SEOPEN instruction specifies a data register storingthe start address of the stream. The SEOPEN instruction also specifies astream template register that stores the stream template as describedabove. The arguments of the SEOPEN instruction are listed in Table 26.

TABLE 26 Argument Description Stream Start Address Scalar registerstoring stream start address Register Stream Number Stream 0 or Stream 1Stream Template Vector register storing stream template data Register

The stream start address register is a register in general scalarregister file 211 (FIG. 2) in this example. The SEOPEN instruction canspecify the stream start address register via src1 field 1303 (FIG. 13)of example instruction coding 1300 (FIG. 13). The SEOPEN instructionspecifies stream 0 or stream 1 in the opcode. The stream templateregister is a vector register in general vector register file 221 inthis example. The SEOPEN instruction can specify the stream templateregister via src2/cst field 1302 (FIG. 13). If the specified stream isactive, the SEOPEN instruction closes the prior stream and replaces thestream with the specified stream.

SECLOSE explicitly marks a stream inactive, flushing any outstandingactivity. Any further references to the stream trigger exceptions.SECLOSE also allows a program to prematurely terminate one or bothstreams.

An SESAVE instruction saves the state of a stream by capturingsufficient state information of a specified stream to restart thatstream in the future. An SERSTR instruction restores a previously savedstream. An SESAVE instruction saves the stream metadata and does notsave any of the stream data. The stream re-fetches stream data inresponse to an SERSTR instruction.

Each stream can be in one of three states: inactive, active, or frozenafter reset. Both streams begin in the inactive state. Opening a streammoves the stream to the active state. Closing the stream returns thestream to the inactive state. In the absence of interrupts andexceptions, streams ordinarily do not make other state transitions. Toaccount for interrupts, the streaming engine adds a third state: frozen.The frozen state represents an interrupted active stream.

In this example, four bits, two bits per stream, define the state ofboth streams. One bit per stream resides within the streaming engine,and the other bit resides within the processor core 110. The streamingengine internally tracks whether each stream holds a parameter setassociated with an active stream. This bit distinguishes an inactivestream from a not-inactive stream. The processor core 110 separatelytracks the state of each stream with a dedicated bit per stream in theTask State Register (TSR): TSR.SE0 for stream 0, and TSR.SE1 forstream 1. These bits distinguish between active and inactive streams.

Opening a stream moves the stream to the active state. Closing a streammoves the stream to the inactive state. If a program opens a new streamover a frozen stream, the new stream replaces the old stream and thestreaming engine discards the contents of the previous stream. Thestreaming engine supports opening a new stream on a currently activestream. The streaming engine discards the contents of the previousstream, flushes the pipeline, and starts fetching data for the newopened stream. Data to the processor is asserted once the data hasreturned. If a program closes an already closed stream, nothing happens.If a program closes an open or frozen stream, the streaming enginediscards all state related to the stream, clears the internalstream-active bit, and clears the counter, tag and address registers.Closing a stream serves two purposes. Closing an active stream allows aprogram to specifically state the stream and the resources associatedwith the stream are no longer needed. Closing a frozen stream alsoallows context switching code to clear the state of the frozen stream,so that other tasks do not see it.

As noted above, there are circumstances when some data within a streamholding register 2818 or 2828 is not valid. As described above, such astate can occur at the end of an inner loop when the number of streamelements is less than the respective stream holding register 2818/2828size or at the end of an inner loop when the number of stream elementsremaining is less than the lanes defined by VECLEN. For times not at theend of an inner loop, if VECLEN is less than the width of stream holdingregister 2818/2828 and GRDUP is disabled, then lanes in stream holdingregister 2818/2828 in excess of VECLEN are invalid.

Referring again to FIG. 28, in this example streaming engine 125 furtherincludes valid registers 2819 and 2829. Valid register 2819 indicatesthe valid lanes in stream head register 2818. Valid register 2829indicates the valid lanes in stream head register 2828. Respective validregisters 2819/2829 include one bit for each minimum ELEM_BYTES lanewithin the corresponding stream head register 2818/2828. In thisexample, the minimum ELEM_BYTES is 1 byte. The preferred data path widthof processor 100 and the data length of stream head registers 2818/2828is 64 bytes (512 bits). Valid registers 2819/2829 accordingly have adata width of 64 bits. Each bit in valid registers 2819/2829 indicateswhether a corresponding byte in stream head registers 2818/2828 isvalid. In this example, a 0 indicates the corresponding byte within thestream head register is invalid, and a 1 indicates the correspondingbyte is valid.

In this example, upon reading a respective one of the stream headregisters 2818/2828 and transferring of data to the requestingfunctional unit, the invalid/valid data in the respective valid register2819/2829 is automatically transferred to a data register withinpredicate register file 234 (FIG. 2) corresponding to the particularstream. In this example the valid data for stream 0 is stored inpredicate register P0 and the valid data for stream 1 is stored inpredicate register P1.

The valid data stored in the predicate register file 234 can be used ina variety of ways. The functional unit can combine the vector streamdata with another set of vectors and then store the combined data tomemory using the valid data indications as a mask, thus enabling thesame process to be used for the end of loop data as is used for caseswhere all the lanes are valid which avoids storing invalid data. Thevalid indication stored in predicate register file 234 can be used as amask or an operand in other processes. P unit 246 (FIG. 2) can have aninstruction to count the number of 1's in a predicate register (BITCNT),which can be used to determine the count of valid data elements from apredicate register.

FIG. 32 illustrates example hardware 3200 to produce the valid/invalidindications stored in the valid register 2819 (FIG. 28). FIG. 32illustrates hardware for stream 0; stream 1 includes correspondinghardware. Hardware 3200 operates to generate one valid word each timedata is updated in stream head register 2818 (FIG. 28). A first inputELTYPE is supplied to decoder 3201. Decoder 3201 produces an outputTOTAL ELEMENT SIZE corresponding to the minimum data size based upon theelement size ELEM_BYTES and whether the elements are real numbers orcomplex numbers. The meanings of various codings of ELTYPE are shown inTable 9. Table 27 shows an example output of decoder 3201 in bytes forthe various ELTYPE codings. Note Table 9 lists bits and Table 27 listsbytes. As shown in Table 27, TOTAL ELEMENT SIZE is 1, 2, 4 or 8 bytes ifthe element is real and 2, 4, 8 or 16 bytes if the element is complex.

TABLE 27 Total Element ELTYPE Real/Complex Size Bytes 0000 Real  1 0001Real  2 0010 Real  4 0011 Real  8 0100 Reserved Reserved 0101 ReservedReserved 0110 Reserved Reserved 0110 Reserved Reserved 1000 Complex, NotSwapped  2 1001 Complex, Not Swapped  4 1010 Complex, Not Swapped  81011 Complex, Not Swapped 16 1100 Complex, Swapped  2 1101 Complex,Swapped  4 1110 Complex, Swapped  8 1111 Complex, Swapped 16

A second input PROMOTE is supplied to decoder 3202. Decoder 3202produces an output promotion factor corresponding to the PROMOTE input.The meaning of various codings of PROMOTE are shown in Table 28, whichshows an example output of decoder 3202 in bytes for the various PROMOTEcodings. The difference in extension type (zero extension or signextension) is not relevant to decoder 3202.

TABLE 28 Promotion PROMOTE Factor 000 1 001 2 010 4 011 8 100 Reserved101 2 110 4 111 8

The outputs of decoders 3201 and 3202 are supplied to multiplier 3203.The product produced by multiplier 3203 is the lane size correspondingto the TOTAL ELEMENT SIZE and the promotion factor. Because thepromotion factor is an integer power of 2 (2^(N)), the multiplicationcan be achieved by corresponding shifting of the TOTAL ELEMENT SIZE,e.g., no shift for a promotion factor of 1, a one-bit shift for apromotion factor of 2, a two-bit shift for a promotion factor of 4, anda three-bit shift for a promotion factor of 8.

NUMBER OF LANES unit 3204 receives the vector length VECLEN and the LANESIZE and generates the NUMBER OF LANES. Table 29 shows an exampledecoding of the number of lanes for lane size in bytes and the vectorlength VECLEN.

TABLE 29 VECLEN LANE SIZE 000 001 010 011 100 101 110  1 1 2 4 8 16 3264  2 — 1 2 4  8 16 32  4 — — 1 2  4  8 16  8 — — — 1  2  4  8 16 — — ——  1  2  4 32 — — — — —  1  2 64 — — — — — —  1

As previously stated, VECLEN must be greater than or equal to theproduct of the element size and the duplication factor. As shown inTable 29, VECLEN must also be greater than or equal to the product ofthe element size and the promotion factor. This means that VECLEN mustbe large enough to guarantee that an element cannot be separated fromits extension produced by type promotion block 2022 (FIG. 20). The cellsbelow the diagonal in Table 29 marked “-” indicate an unpermittedcombination of parameters.

The NUMBER OF LANES output of unit 3204 serves as one input toLANE/REMAINING ELEMENTS CONTROL WORD unit 3211. A second input comesfrom multiplexer 3212. Multiplexer 3212 receives a Loop0 input and aLoop1 input. The Loop0 input and the Loop1 input represent the number ofremaining elements in the current iteration of the corresponding loop.

FIG. 33 illustrates a partial schematic view of address generator 2811shown in FIG. 28. Address generator 2811 forms an address for fetchingthe next element in the defined stream of the corresponding streamingengine. Start address register 3301 stores a start address of the datastream. As previously described above, in this example, start addressregister 3301 is a scalar register in global scalar register file 211designated by the SEOPEN instruction that opened the correspondingstream. The start address can be copied from the specified scalarregister and stored locally at the respective address generator2811/2821 by control logic included with address generator 2811. Thefirst loop of the stream employs Loop0 count register 3311, adder 3312,multiplier 3313 and comparator 3314. Loop0 count register 3311 storesthe working copy of the iteration count of the first loop (Loop0). Foreach iteration of Loop0, adder 3312, as triggered by the Next Addresssignal, adds 1 to the loop count, which is stored back in Loop0 countregister 3311. Multiplier 3313 multiplies the current loop count and thequantity ELEM_BYTES. ELEM_BYTES is the size of each data element inloop0 in bytes. Loop0 traverses data elements physically contiguous inmemory with an iteration step size of ELEM_BYTES.

Comparator 3314 compares the count stored in Loop0 count register 3311(after incrementing by adder 3313) with the value of ICNT0 2901 (FIG.29) from the corresponding stream template register 2900 (FIG. 29). Whenthe output of adder 3312 equals the value of ICNT0 2901 of the streamtemplate register 2900, an iteration of Loop0 is complete. Comparator3314 generates an active Loop0 End signal. Loop0 count register 3311 isreset to 0 and an iteration of the next higher loop, in this case Loop1,is triggered.

Circuits for the higher loops (Loop1, Loop2, Loop3, Loop4 and Loop5) aresimilar to that illustrated in FIG. 33. Each loop includes a respectiveworking loop count register, adder, multiplier and comparator. The adderof each loop is triggered by the loop end signal of the prior loop. Thesecond input to each multiplier is the corresponding dimension DIM1,DIM2, DIM3, DIM4 and DIM5 from the corresponding stream template. Thecomparator of each loop compares the working loop register count withthe corresponding iteration value ICNT1, ICNT2, ICNT3, ICNT4 and ICNT5of the corresponding stream template register 2900. A loop end signalgenerates an iteration of the next higher loop. A loop end signal fromLoop5 ends the stream.

FIG. 33 also illustrates the generation of Loop0 count. Loop0 countequals the updated data stored in the corresponding working countregister 3311. Loop0 count is updated on each change of working Loop0count register 3311. The loop counts for the higher loops (Loop1, Loop2,Loop3, Loop4 and Loop5) are similarly generated.

FIG. 33 also illustrates the generation of Loop0 address. Loop0 addressequals the data output from multiplier 3313. Loop0 address is updated oneach change of working Loop0 count register 3311. Similar circuits forLoop1, Loop2, Loop3, Loop4 and Loop5 produce corresponding loopaddresses. In this example, Loop0 count register 3311 and the other loopcount registers are implemented as count up registers. In anotherexample, initialization and comparisons operate as count down circuits.

Referring again to FIG. 32, the value of the loop down count, such asLoop0/, is given by Loopx/=ICNTx−Loopx, i.e., the loop down count is thedifference between the initial iteration count specified in the streamtemplate register and the loop up count produced as illustrated in FIG.33.

LANE/REMAINING ELEMENTS CONTROL WORD unit 3211 (FIG. 32) generates acontrol word 3213 based upon the number of lanes from NUMBER OF LANESunit 3204 and the loop down count selected by multiplexer 3212. Thecontrol input to multiplexer 3212 is the TRANSPOSE signal from field3002 of FIG. 30. If TRANSPOSE is disabled (“000”), multiplexer 3212selects the Loop0 down count Loop0/. For all other legal values ofTRANSPOSE (“001”, “010”, “011”, “100”, “101” and “110”) multiplexer 3212selects the Loop1 down count Loop1/.

The streaming engine maps the innermost dimension to consecutive lanesin a vector. For normal streams this is Loop0. For transposed streams,this is Loop1, because transposition exchanges the two dimensions.

LANE/REMAINING ELEMENTS CONTROL WORD unit 3211 generates control word3213 as follows. Control word 3213 has a number of bits equal to thenumber of lanes from unit 3204. If the remaining count of elements ofthe selected loop is greater than or equal to the number of lanes, thenall lanes are valid. For this case, control word 3213 is all ones,indicating that all lanes within the vector length VECLEN are valid. Ifthe remaining count of elements of the selected loop is nonzero and lessthan the number of lanes, then some lanes are valid and some areinvalid. According to the lane allocation described above in conjunctionwith FIGS. 21 and 22, stream elements are allocated lanes starting withthe least significant lanes. Under these circumstances, control word3213 includes a number of least significant bits set to one equal to thenumber of the selected loop down count. All other bits of control word3213 are set to zero. In the example illustrated in FIG. 32, the numberof lanes equals eight and there are five valid (1) least significantbits followed by three invalid (0) most significant bits whichcorresponds to a loop having five elements remaining in the finaliteration.

Control word expansion unit 3214 expands the control word 3213 basedupon the magnitude of LANE SIZE. The expanded control word includes onebit for each minimally sized lane. In this example, the minimum streamelement size, and thus the minimum lane size, is one byte (8 bits). Inthis example, the size of holding registers 2818/2828 equals the vectorsize of 64 bytes (512 bits). Thus, the expanded control word has 64bits, one bit for each byte of stream holding registers 2818/2828. Thisexpanded control word fills the least significant bits of thecorresponding valid register 2819 and 2829 (FIG. 28).

For the case when VECLEN equals the vector length, the description iscomplete. The expanded control word includes bits for all places withinrespective valid register 2819/2829. There are some additionalconsiderations when VECLEN does not equal the vector length. When VECLENdoes not equal the vector length, the expanded control word does nothave enough bits to fill the corresponding valid register 2819/2829. Asillustrated in FIG. 32, the expanded control word fills the leastsignificant bits of the corresponding valid register 2819/2829, thusproviding the valid/invalid bits for lanes within the VECLEN width.Another mechanism is provided for lanes beyond the VECLEN width up tothe data width of stream head register 2818.

Referring still to FIG. 32, multiplexer 3215 and group duplicate unit3216 are illustrated to provide the needed additional valid/invalidbits. Referring to the description of VECLEN, if group duplication isnot enabled (GRDUP=0), then the excess lanes are not valid. A firstinput of multiplexer 3215 is an INVALID 0 signal that includes multiplebits equal in number to VECLEN. When GRDUP=0, multiplexer 3215 selectsthis input. Group duplicate unit 3216 duplicates this input to allexcess lanes of stream head register 2818. Thus, the most significantbits of valid register 2819 are set to zero indicating the correspondingbytes of stream head register 2818 are invalid. This occurs for vectors1-8 of the example shown in Table 15, vectors 1-15 of the example shownin Table 16, and vectors 1-29 of the example shown in Table 17.

In another example, mux 3215 and group duplicate block 3216 are replacedwith group duplicate logic that is similar to the group duplicate logic2025 illustrated in FIG. 31.

As previously described, if group duplication is enabled (GRDUP=1), thenthe excess lanes of stream head register 2818 (FIG. 28) are filled withcopies of the least significant bits. A second input of multiplexer 3215is the expanded control word from control word expansion unit 3214. WhenGRDUP=1, multiplexer 3215 selects this input. Group duplicate unit 3216duplicates this input to all excess lanes of stream head register 2818.

There are two possible outcomes. In one outcome, in most cases, all thelanes within VECLEN are valid and the bits from control word expansionunit 3214 are all ones. This occurs for vectors 1-7 of the groupduplication example shown in Table 18 and vectors 1-14 of the groupduplication example shown in Table 19. Under these conditions, all bitsof the expanded control word from control word expansion unit 3214 areone and all lanes of stream head register 2818 are valid. Groupduplicate unit 3216 thus fills all the excess lanes with ones. In theother outcome, the number of remaining stream data elements is less thanthe number of lanes within VECLEN. This occurs for vector 8 in the groupduplication example shown in Table 18 and vector 15 in the groupduplication example shown in Table 19. Under these conditions, somelanes within VECLEN are valid and some are invalid. Group duplicate unit3216 fills the excess lanes with bits having the same pattern as theexpanded control word bits. In either case, the excess lanes are filledcorresponding to the expanded control bits.

Referring still to FIG. 32, a boundary 3217 is illustrated between theleast significant bits and the most significant bits. The location ofthis boundary is set by the size of VECLEN relative to the size ofstream head register 2818.

FIG. 34 is a partial schematic diagram 3400 illustrating the streaminput operand coding described above. In this example, the stream inputoperand is encoded using the lower five bits of the six bit operandfield and the sixth bit is ignored. In other examples, all six bits ofan operand field are used to encode the stream input operand. FIG. 34illustrates a portion of instruction decoder 113 (see FIG. 1) decodingsrc1 field 1303 of one instruction to control corresponding src1 inputof functional unit 3420. These same or similar circuits are duplicatedfor src2/cst field 1302 of an instruction controlling functional unit3420. In addition, these circuits are duplicated for each instructionwithin an execute packet capable of employing stream data as an operandthat are dispatched simultaneously.

Instruction decoder 113 receives the src1 field 1303 of an instruction.The opcode field 1304 and the unit field 1305 specify a correspondingfunctional unit 3420 and the function to be performed. In this example,functional unit 3420 can be L2 unit 241, S2 unit 242, M2 unit 243, N2unit 244 or C unit 245. The relevant part of instruction decoder 113illustrated in FIG. 34 decodes src1 field 1303. Sub-decoder 3411determines whether the src1 field 1303 is in the range from 00000 to01111. If this is the case, sub-decoder 3411 supplies a correspondingregister number to global vector register file 231. In this example, theregister number is the four least significant bits of the src1 field1303. Global vector register file 231 recalls data stored in theregister corresponding to the register number and supplies the data tothe src1 input of functional unit 3420.

Sub-decoder 3412 determines whether the src1 field 1303 is in the rangefrom 10000 to 10111. If this is the case, sub-decoder 3412 supplies acorresponding register number to the corresponding local vector registerfile. If the instruction is directed to L2 unit 241 or S2 unit 242, thecorresponding local vector register file is local vector register file232. If the instruction is directed to M2 unit 243, N2 unit 244 or Cunit 245, the corresponding local vector register file is local vectorregister file 233. In this example, the register number is the threeleast significant bits of the src1 field 1303. The corresponding localvector register file 232/233 recalls data stored in the registercorresponding to the register number and supplies the data to the src1input of functional unit 3420.

Sub-decoder 3413 determines whether the src1 field 1303 is 11100. Ifthis is the case, sub-decoder 3413 supplies a stream 0 read signal tostreaming engine 125. Streaming engine 125 then supplies stream 0 datastored in holding register 2818 to the src1 input of functional unit3420.

Sub-decoder 3414 determines whether the src1 field 1303 is 11101. Ifthis is the case, sub-decoder 3414 supplies a stream 0 read signal tostreaming engine 125. Streaming engine 125 then supplies stream 0 datastored in holding register 2818 to the src1 input of functional unit3420. Sub-decoder 3414 also supplies an advance signal to stream 0. Aspreviously described, streaming engine 125 advances to store the nextsequential vector of data elements of stream 0 in holding register 2818.

Supply of a stream 0 read signal to streaming engine 125 by eithersub-decoder 3413 or sub-decoder 3414 triggers another data movement.Upon such a stream 0 read signal, streaming engine 125 supplies the datastored in valid register 2819 to predicate register file 234 forstorage. In accordance with this example, this is a predetermined dataregister within predicate register file 234. In this example, dataregister P0 corresponds to stream 0.

Sub-decoder 3415 determines whether the src1 field 1303 is 11110. Ifthis is the case, sub-decoder 3415 supplies a stream 1 read signal tostreaming engine 125. Streaming engine 125 then supplies stream 1 datastored in holding register 2828 to the src1 input of functional unit3420.

Sub-decoder 3416 determines whether the src1 field 1303 is 11111. Ifthis is the case, sub-decoder 3416 supplies a stream 1 read signal tostreaming engine 125. Streaming engine 125 then supplies stream 1 datastored in holding register 2828 to the src1 input of functional unit3420. Sub-decoder 3414 also supplies an advance signal to stream 1. Aspreviously described, streaming engine 125 advances to store the nextsequential vector of data elements of stream 1 in holding register 2828.

Supply of a stream 1 read signal to streaming engine 125 by eithersub-decoder 3415 or sub-decoder 3416 triggers another data movement.Upon such a stream 1 read signal, streaming engine 125 supplies the datastored in valid register 2829 to predicate register file 234 forstorage. In accordance with this example, this is a predetermined dataregister within predicate register file 234. In this example, dataregister P1 corresponds to stream 1.

Similar circuits are used to select data supplied to src2 input offunctional unit 3402 in response to the bit coding of src2/cst field1302. The src2 input of functional unit 3420 can be supplied with aconstant input in a manner described above. If instruction decoder 113generates a read signal for stream 0 from either src1 field 1303 orsrc2/cst field 1302, streaming engine 125 supplies the data stored invalid register 2819 to predicate register P0 of predicate register file234 for storage. If instruction decode 113 generates a read signal forstream 1 from either src1 field 1303 or src2/cst field 1302, streamingengine 125 supplies the data stored in valid register 2829 to predicateregister P1 of predicate register file 234 for storage.

The exact number of instruction bits devoted to operand specificationand the number of data registers and streams are design choices. Inparticular, the specification of a single global vector register fileand omission of local vector register files is feasible. This exampleemploys a bit coding of an input operand selection field to designate astream read and another bit coding to designate a stream read andadvancing the stream.

The process illustrated in FIG. 34 automatically transfers valid datainto predicate register file 234 each time stream data is read. Thetransferred valid data can then be used by P unit 246 for furthercalculation of meta data. The transferred valid data can also be used asa mask or as an operand for other operations by one or more of vectordata path side B 116 functional units including L2 unit 241, S2 unit242, M2 unit 243, N2 unit 244 and C unit 245. There are numerousfeasible compound logic operations employing this stream valid data.

FIG. 35 is a partial schematic diagram 3500 illustrating another exampleconfiguration for selecting operand sources. In this example, the streaminput operand is encoded using the lower five bits of the six bitoperand field and the sixth bit is ignored. In other examples, all sixbits of an operand field are used to encode the stream input operand. Inthis example, the respective stream valid register 2819/2829 need not beautomatically loaded to a predetermined register in predicate registerfile 234. Instead, an explicit instruction to P unit 246 is used to movethe data. FIG. 35 illustrates a portion of instruction decoder 113 (seeFIG. 1) decoding src1 field 1303 of one instruction to control acorresponding src1 input of P unit 246. These same or similar circuitscan be duplicated for src2/cst field 1302 (FIG. 13) of an instructioncontrolling P unit 246.

Instruction decoder 113 receives the src1 field 1303 of an instruction.The opcode field opcode field 1304 and the unit field 1305 specify Punit 246 and the function to be performed. The relevant part ofinstruction decoder 113 illustrated in FIG. 35 decodes the src1 field1303. Sub-decoder 3511 determines whether the src1 field 1303 is in therange 00000 to 01111. If this is the case, sub-decoder 3511 supplies acorresponding register number to global vector register file 231. Inthis example, the register number is the four least significant bits ofthe src1 field 1303. Global vector register file 231 recalls data storedin the register corresponding to the register number and supplies thedata to the src1 input of P unit 246.

Sub-decoder 3512 determines whether the src1 field 1303 is in the range10000 to 10111. If this is the case, sub-decoder 3512 supplies a decodedregister number to the predicate register file 234. In this example, theregister number is the three least significant bits of the src1 field1303. The predicate register file 234 recalls data stored in theregister corresponding to the register number and supplies the data tothe src1 input of predicate unit 246.

Sub-decoder 3513 determines whether the src1 field 1303 is 11100. Ifthis is the case, sub-decoder 3513 supplies a stream 0 valid read signalto streaming engine 125. Streaming engine 125 then supplies valid datastored in valid register 2819 to the src1 input of P unit 246.

Sub-decoder 3514 determines whether the src1 field 1303 is 11101. Ifthis is the case, sub-decoder 3514 supplies a stream 1 valid read signalto streaming engine 125. Streaming engine 125 then supplies stream 1valid data stored in valid register 2829 to the src1 input of P unit246.

The P unit 246 instruction employing the stream valid register 2819/2829as an operand can be any P unit instruction previously described such asNEG, BITCNT, RMBD, DECIMATE, EXPAND, AND, NAND, OR, NOR, and XOR.

The special instructions noted above can be limited to P unit 242. Thus,the operations outlined in FIGS. 34 and 35 can be used together. If thefunctional unit specified by the instruction is L2 unit 241, S2 unit242, M2 unit 243, N2 unit 244 or C unit 245, then the src1 field 1303 isinterpreted as outlined with respect to FIG. 34. If the functional unitspecified by the instruction is P unit 246, then the src1 field 1303 isinterpreted as outlined with respect to FIG. 35. Alternatively, theautomatic saving of the stream valid register to a predeterminedpredicate register illustrated in FIG. 34 can be implemented in oneexample and not implemented in another example.

As previously described in reference to FIGS. 1 and 2, the processingunit core 110 includes scalar data path side A 115 and vector data pathside B 116. The scalar data path 115 is organized as one 64-bit sliceand the vector data path 116 is organized into eight 64-bit slices thattaken together correspond to an entire 512-bit vector. Each data pathslice corresponds to a 64-bit SIMD lane. Scalar data path side A 116includes M1 unit 223 and N1 unit 224 and vector data path side B 116includes M2 unit 243 and N2 unit 244. Each of the M1, N1, M2, and N2units may be referred to generally as a multiplication unit hereinalthough a unit may include circuitry for operations other thanmultiplication.

In some examples, the multiplication units 223, 224 in scalar data pathside A 115 include identical multiplication circuitry and themultiplication units 243, 244 in vector data path side B 116 includeidentical multiplication circuitry. The multipliers in themultiplication circuitry each produce Wallace tree outputs based on theinputs to the multipliers. Note that each multiplier output is not thecomplete result of a*b but rather is the final two compressed products,referred to as the partial sum and partial carry, from a Wallace tree.In other words, each multiplier outputs two 32-bit words that when addedtogether produce the actual product. Additional description of suchmultipliers may be found, for example, in U.S. Pat. No. 8,918,445,issued Dec. 23, 2014, which is incorporated by reference herein in itsentirety. Additional description of Wallace tree multipliers may also befound in P. Kumawat and G. Sujediya, “Design and Comparison of 8×8Wallace Tree Multiplier using CMOS and GDI Technology,” IOSR Journal ofVLSI and Signal Processing, Vol. 7, Issue 4, pp. 57-62, July-August2017.

In the example processing unit core 110, rather than having a singlemultiplication unit in each data path 115, 116 that controls all themultipliers in the data path, each data path has two multiplicationunits, e.g., M1/N1 223, 224 and M2/N2 243, 244, with some number of16-bit by 16-bit multipliers split between the two units. Morespecifically, in this example, each data path 115, 116 includes sixteen16-bit by 16-bit multipliers for each 64-bit wide slice supported by thedata path. In the scalar data path 115, which supports one 64-bit wideslice, the multiplication units 223, 224 each include eight 16-bit by16-bit multipliers. In the vector data path 116, which supports eight64-bit wide slices, the multiplication units 243, 244 each includesixty-four 16-bit by 16-bit multipliers. Other examples may have more orfewer multipliers per multiplication unit and/or the maximum number ofbits handled by the multipliers may differ.

FIG. 36 illustrates an example configuration of multipliers for a single64-bit wide slice of a data path. In this example, the M unit slicemultiply component 3600 includes two multiply clusters 3604, 3606 andthe N unit slice multiply component 3602 includes two multiply clusters3608, 3610. Each multiply cluster 3604, 3606, 3608, 3610 includesmasking logic, an array of four 16-bit by 16-bit multipliers, a set ofmultiplexers coupled to the outputs of the multipliers, and a compressor(carry-save adder) coupled to the outputs of the set of multiplexers.Each input to a multiply cluster 3604, 3606, 3608, 3610 passes throughthe respective masking logic before being routed to respectivemultiplier arrays. The masking logic is configurable to mask off unusedbits in the inputs for some multiplication operations.

Each set of multiplexers is configurable to arrange the outputs of thecoupled multipliers for input into the respective compressor as neededfor the particular instruction being executed. Each multiply cluster3604, 3606, 3608, 3610 is configured to perform, for example, four 16×16multiplies, one 32×32 multiply, one 32×32 complex multiply, or one 32×32complex multiply with one operand conjugated.

Additional compressors (carry-save adders) 3616, 3618 are coupled viarespective partial product alignment multiplexing logic 3612, 3614 toreceive the outputs of two respective multiply clusters 3604, 3606,3608, 3610. The partial product alignment multiplexing logic 3614 isalso coupled to receive outputs of the multiply clusters 3604, 3606 andthe compressor 3618 is configured to generate, for example, one 64×64bit multiply for double precision floating point, one 64×64 complexmultiply, or one 64×64 complex multiply with one operand conjugatedusing the outputs of the coupled multiply clusters.

The compressors 3616, 3618 are also configurable to add two sets ofpartial sum and partial carry from respective multiply clusters 3604,3606, 3608, 3610 for 32×32 dot product operations, 8-tap finite impulsefilter (FIR) operations, or matrix multiplication operations. The FIRinstructions and matrix multiplication instructions that operate on themultiplication units 223, 224 are described in more detail herein.Outputs of the compressors 3616, 3618 are 128-bit partial sum and128-bit partial carry.

Four 64-bit wide multiplexers are coupled to each compressor 3616, 3618to either select the partial sum and partial carry output from therespective compressor 3616, 3618 or bypass the output based on theinstruction being performed. The 128-bit adders 3617, 3619 areconfigured to calculate final products by adding the partial sum andpartial carry using carry propagating addition. The adders 3617, 3619are configured to perform four 32-bit additions, two 64-bit additions,or one 128-bit addition based on the instruction being executed. One52-bit double precision floating point mantissa, two 23-bit singleprecision floating point mantissas, one 64-bit integer, or two 32-bitintegers can be extracted from the output of the adder 3619. The outputof the adder 3617 is the same as that of the adder 3619 except that theadder 3617 does not output a double precision mantissa. The final resultmultiplexers 3621, 3623 are coupled to respective adders 3617, 3619 toreceive the outputs and are configured to produce a final result basedon the instruction being executed.

As previously mentioned herein, multiply instructions may be singleissue instructions or dual issue instructions. Single issue instructionsare those instructions that can be executed independently in eachmultiplication unit 223, 224, 243, 244 in a data path 115, 116, i.e.,instructions that can be executed on a single multiplication unit. In asingle issue multiply instruction, the number of 16×16 or 8×8 multipliesneeded for the instruction is not greater than what a multiplicationunit provides for a slice. Further, two single issue multiplyinstructions can be executed independently and in parallel—e.g., one inM2 unit 243 and the other in N2 unit 244—to achieve 2× multiplythroughput and hence 2× performance. For example, consider a complexmultiply instruction that multiplies 16 complex elements with 16 complexelements in which each element is 16-bit real and 16-bit imaginary andproduces 16 complex outputs in which each output is 16-bit real and16-bit imaginary after rounding. With two multiplication units in a datapath, two such complex multiply instructions can be executed in parallelin the data path to produce 32 complex outputs.

In another example, consider an algorithm that uses a vector multiplyoperation which multiplies half-words. With a single multiplicationunit, only one multiply per clock cycle can be executed. With 32 halfwords per vector, performance is limited to 32 multiplies per clockcycle, even though the single multiplication unit contains 128 16×16multipliers. With two multiplication units each having 64 16×16multipliers, 64 multiplies can be executed per clock cycle. In anotherexample, a 32-bit single-precision vector multiply instruction utilizesonly 64 16×16 multipliers as there are sixteen single-precision numbersin each vector, and each takes four 16×16 multipliers. With twomultiplication units, instruction throughput can be doubled by using 12816×16 multipliers.

A dual issue instruction is an instruction that is executed on bothmultiplication units in a data path such that the multiplication unitsare “coupled”, i.e., execute in lock-step, to share the multipliers inboth units as needed to complete instruction execution. For example, adual issue instruction may be an instruction that requires more thaneight 16×16 or sixteen 8×8 multiplications per slice. In some examples,a dual issue instruction is issued on the multiplication unit N1 224 orN2 244 and, in response, the respective multiplication unit M1 223 or M2243 is dedicated to executing the dual issue instruction and no otherinstruction can be issued to the multiplication unit M1 or M2 untilexecution of the dual issue instruction is complete. Arrows 3620 in FIG.36 show internal communication between the two slices of both units usedto complete dual issue instructions. Also, for dual issue vectorinstructions which do not require more than the available multipliers ina single multiplication unit but produce 1024-bit double vector outputs,the M2 multiplication unit 243 and the N2 multiplication unit 244 can beused together such that the most-significant 512-bit result iscommunicated via the M2 unit 243 result bus and the least-significant512-bit result is communicated via the N2 unit 244 result bus.

If an operand of a dual issue instruction is dual vector, e.g., a1024-bit operand, the LSB vector is dispatched to the N2 unit and theMSB vector is dispatched to the M2 unit. If an operand is a singlevector, the vector is dispatched on both the M2 unit and the N2 unit.This approach helps reduce the input wires routing back and forthbetween the M2 and N2 units and improves frequency, area and power. Ifthe output of a dual issue instruction is dual vector, the LSB vectoroutput is issued from the N2 unit and the MSB vector output is issuedfrom the M2 unit. If output is a single vector, the output is issuedfrom the N2 unit.

FIG. 37 illustrates an instruction format for a dual issue instruction.The DST field 3700 designates the destination register for the result,the SRC2 3702 and SRC1 3704 fields designate the operand registers, theopcode field 3708 designates the opcode of the instruction (whichencodes the functional unit), and the V bit field 3710 indicates whetheror not the instruction is a dual issue instruction. If the V bit 3710 is1, the instruction is a dual issue instruction that is dispatched toboth the M and N units; otherwise, the instruction is a single issueinstruction that is dispatched according to the functional unit encodedin the opcode 3708.

FIG. 38 illustrates a block diagram of the data flow for instructiondecoding in the N unit and the M unit. Decoding an instruction in the Nunit is the same for a dual issue and a single issue instruction.However, decoding an instruction in M unit is more involved for a dualissue instruction. This involvement comes in the form of generating theregister read and write enables from the SRC1, SRC2, DST registers andthe conditional execution of the instruction using the scalarpredication register using the CREGZ format.

For a dual issue instruction, if the DST field 3700 (FIG. 37) designatesa single register, the write to that register happens only from the Nunit and the write enable for the M unit is suppressed. If the DST field3700 designates a register pair such as VB1 and VB0, the M unit writesto VB1 and the N unit writes to VB0. If the SRC2 field 3702 and/or theSRC1 field 3704 designate a register pair such as VB1 and VB0, the Munit reads from VB1, and the N unit reads from VB0. If a source fielddesignates a single register, then both the N and M units read from thedesignated register.

As previously described herein, the CREGZ instruction format specifiesthe predication register to be used on a per unit basis for theconditional execution of an instruction. If the execute packet includesthe CREGZ instruction format, then depending on the scalar predicationinformation specified, the instruction will be conditionally executed.For a dual issue instruction, the N unit scalar predication informationfrom the CREGZ instruction format (if present in the execute packet) isused for both M and N units and any M unit information is ignored.

Referring again to FIG. 36, consider the execution of an example dualissue vector multiply instruction that specifies SRC1 and SRC2 operandsof 512-bit vectors (VB0, VB1) with eight 64-bit SIMD lanes, and produceseight 64-bit SIMD outputs. For each 64-bit output, the product of two64-bit elements that are in a 64-bit SIMD lane is calculated. Sixteen16×16 multiplies are needed to calculate a 64-bit×64-bit product.Because each N2 unit slice multiply component has eight 16×16multipliers, the eight 16×16 multipliers from the respective M2 unitslice multiply component are shared for execution of the instruction. AnM2 unit slice multiply component computes the partial product of the 32least significant bits of the 64-bit elements and the respective N2 unitslice multiply component computes the partial product of 32 mostsignificant bits of the 64-bit elements. The partial products are routedfrom each M2 unit slice multiply component to the respective N2 unitslice multiply component where the partial products are aligned beforethe compressor produces the partial sum and partial carry. The adder inthe N2 unit slice multiply component uses these two terms as inputs toproduce the final product that is subsequently stored in the respectiveSIMD lane of the location specified in the DST field of the instruction.

The M1/N1 units 223, 224 and M2/N2 units 243, 244 of the processing unitcore 110 are designed to handle single precision (SP) and doubleprecision (DP) floating point multiply with both normal and subnormalnumbers according to the IEEE Standard for Floating-Point Arithmetic(IEEE 754) in IEEE Std 754-2008, IEEE Computer Society, Aug. 29, 2008,pp. 1-70. The products of the single or double precision mantissas, alsoreferred to as significands, are computed on shared 16×16 multipliers inthe M and N units. As is described in more detail herein, a floatingpoint pipeline in each set of slice multiply logic in the multiplicationunits 223, 224, 243, 244, e.g., M unit slice multiply component 3600 andN unit slice multiply component 3602, executes in parallel to the mainexecution path of the slice multiply logic to perform some floatingpoint operations.

Each N unit slice multiply component and M unit slice multiply componentin a data path 115, 116 can perform two single issue IEEE SP floatingpoint multiply operations, i.e., the M2 unit 243 and N2 unit 244 in thevector data path 116 can each generate sixteen SP floating pointproducts and the M1 unit 223 and N1 unit 224 in the scalar data path 115can each generate two SP floating point products. The IEEE DP multiplyinstruction is dual issue so each pair of M and N unit multiply slicecomponents can perform one IEEE DP floating point multiply, i.e., the M2unit 243 and N2 unit 244 in the vector data path 116 can generate eightDP floating point products and the M1 unit 223 and N1 unit 224 in thescalar data path 115 can generate one DP floating point product.

Subnormal numbers, also referred to as denormal numbers, are very smallfloating point values near zero. Formally, subnormal numbers are numberssmaller than those that can be represented without leading zeros in themantissa, e.g., normal numbers. Typically, floating point numbers arerepresented without leading zeros in the mantissa. Instead, the leadingzeros in the number are moved to the exponent, e.g., 0.0123 isrepresented as 1.23×10⁻². Subnormal numbers are therefore numbers inwhich such a representation would cause the exponent to be lower thanthe minimum possible value. In such a situation, the mantissa is forcedto have leading zeros. Thus, a subnormal number is represented with abiased exponent of all 0 bits, which represents an exponent of −126 insingle precision (not −127), or −1022 in double precision (not −1023).In contrast, the smallest biased exponent representing a normal numberis 1.

Accordingly, the binary representation of a floating point number ismade unique by choosing the smallest representable exponent allowing thevalue to be represented exactly. Further, the exponent is notrepresented directly, but a bias is added so that the smallestrepresentable exponent is represented as 1, with 0 used for subnormalnumbers. For numbers with an exponent in the normal range (the exponentfield being not all ones or all zeros), the leading bit of the mantissawill always be 1. Consequently, a leading 1 can be implied rather thanexplicitly present in the memory encoding of the mantissa, and under thestandard the explicitly represented part of the significand will liebetween 0 and 1. Implying the leading 1 allows the binary format to havean extra bit of precision and may be referred to as leading bitconvention, implicit bit convention, or hidden bit convention. Further,a mantissa with an implied leading bit may be referred to as a leadingbit encoded mantissa herein.

An IEEE floating point product may be computed as follows: 1) determinethe value of the implied bit for each operand, which indicates whetheror not an operand is a subnormal number; 2) generate the product ofmantissas with implied bits; 3) normalize the mantissa product followedby sticky bit computation and rounding; 4) extract the final mantissa;5) conditionally add 1 to the final exponent based on the result afterrounding; 6) determine the final sign by XOR of the signs of the twooperands; and 7) output the final product: [sign] [exponent] [mantissa].

The previously mentioned floating point pipelines perform some of theabove computations for determining the floating point product. Referringagain to FIG. 36, the floating point pipeline for an M unit slicemultiply component 3600 and an N unit slice multiply component 3602 areidentical and include an IEEE mode floating point path and aflush-to-zero (FTZ) path. Each IEEE mode floating point path includes aleading zeros count component 3622, 3624, a shift amount computationcomponent 3626, 3628, and a normalization component 3630, 3632, twoimplied bit determination components 3642, 3643, 3644, 3645, and twoimplied bit partial product computation components 3646, 3647, 3648,3649. Each FTZ path includes an exponent calculation component 3634,3636, and an exponent adjustment component 3638, 3640.

To handle the multiplication of subnormal numbers, the number of leadingzeros in the mantissas of the operands is important as the mantissaproduct is shifted by an amount that is based on the number of leadingzeros in the mantissas to generate a normal number. The leading zeroscount components 3622, 3624 are configured to detect the number ofleading zeroes in the mantissas of the respective operands. For SPfloating point multiplies, the leading zeros count components 3622, 3624can determine the number of leading zeros for both SP multiplies. Forthe dual issue DP floating point multiplies, the leading zeros countcomponent 3624 determines the number of leading zeros. The leading zeroscount components are also configured to perform exponent calculation,special value handling, and sign handling.

The shift amount computation components 3626, 3628 are configured todetermine a right shift amount for each mantissa product based on therespective number of leading zeroes detected by the leading zeros countcomponents 3622, 3624. The right shift amount for an SP floating pointmultiply, RSSP, is determined as per

$\begin{matrix}{{{RSSP} = {\left( {48 - {LZC}} \right) - \left( {25 - \left\{ {\left( {{EP} - {LZC}} \right) - {E\; \min}} \right\}} \right)}},} \\{{{{{when}\mspace{14mu} {EP}} - {LZC}} < {E\; \min}}} \\{\left. {= {23 - {LZC} + \left\{ {\left( {{EP} - {LZC}} \right) - {E\; \min}} \right)}} \right\},} \\{{{{{when}\mspace{14mu} {EP}} - {LZC}} < {E\; \min}}} \\{{= {23 - {LZC}}},{{{{when}\mspace{14mu} {EP}} - {LZC}}>={E\; \min}}}\end{matrix}\quad$

and the right shift amount for a DP floating point multiply, RSDP, isdetermines as per

$\begin{matrix}{{{RSDP} = {\left( {106 - {LZC}} \right) - \left( {54 - \left\{ {\left( {{EP} - {LZC}} \right) - {E\; \min}} \right\}} \right)}},} \\{{{{{when}\mspace{14mu} {EP}} - {LZC}} < {E\; \min}}} \\{\left. {= {52 - {LZC} + \left\{ {\left( {{EP} - {LZC}} \right) - {E\; \min}} \right)}} \right\},} \\{{{{{when}\mspace{14mu} {EP}} - {LZC}} < {E\; \min}}} \\{{= {52 - {LZC}}},{{{{when}\mspace{14mu} {EP}} - {LZC}}>={E\; \min}}}\end{matrix}\quad$

where LZC is the leading zero count, EP is the biased exponent of theproduct, and Emin is the minimum biased exponent of the product. Theright shift amounts are provided to the respective normalizationcomponents 3630, 3632. For SP floating point multiplies, the shiftamount computation components 3626, 3628 can determine the shift amountfor both SP multiplies. For the dual issue DP floating point multiplies,the shift computation component 3628 determines the shift amount.

The normalization components 3630, 3632 are configured to performnormalization of the mantissa product output by the respective adders3617, 3619 using the right shift amounts from the respective shiftamount computation components 3626, 3628, sticky bit determination androunding after normalization, exponent adjustment after rounding,special value handling, and sign handling. The resulting mantissa andexponent are provided to the respective final result multiplexer 3621,3623. For SP floating point multiplies, the normalization components3630, 3632 can perform the above operations for both SP multiplies. Forthe dual issue DP floating point multiplies, the normalization component3632 determines performs the above operations.

The exponent calculation components 3634, 3636 and the exponentadjustment components 3638, 3640 are configured to calculate exponentsfor flush-to-zero (FTZ) mode. In FTZ mode, denormalized operands arereplaced with zeroes. The exponent calculation components 3634, 3636 areconfigured to calculate exponents and perform sign handling and specialvalue handling in FTZ mode and the exponent adjustment components 3638,3640 are configured to perform exponent adjustment due to roundingand/or sign handling and/or special value handling. The exponentadjustment components 3638, 3640 provide final exponents and specialvalue and sign information and adders 3617, 3619 provide correspondingmantissas to respective final result multiplexers 3621, 3623. Theexponent adjustment components 3638, 3640 also share some informationwith normalization components 3630, 3632, e.g., special value and signinformation.

The implied bit determination components 3642, 3643, 3644, 3645 and theimplied bit partial product calculation components 3646, 3647, 3648,3649 are configured to perform implied bit detection and computation ofthe mantissa partial products attributable to the implied bits inparallel with computation of the partial products of the remainder ofthe mantissa bits, i.e., the leading bit encoded mantissas, by themultipliers. As previously mentioned, prior to executing floating pointmultiplication operations, the value of the implied bit is determined.This may be done by determining if the exponent of an operand is zero. Azero value exponent indicates that the operand is subnormal and theimplied bit is zero. A non-zero value exponent indicates that theoperand is normal and the implied bit is one.

Each of the implied bit determination components 3642, 3643, 3644, 3645is configured to perform this determination for respective operands andprovide the results to the implied bit partial product calculationcomponents 3646, 3647, 3648, 3649. In some examples, the implied bitdetermination components 3642, 3643, 3644, 3645 implement the impliedbit determination as a bit-wise OR-reduction of the exponent bitsimplemented with four levels of 2-input OR gates.

Further, the computation of the partial product terms of the mantissasof the operands can be split into computation of the partial productterms of the mantissas without the respective implied bits, i.e., theleading bit encoded mantissas, and computation of the partial productterms attributable to the implied bits. For example, let the two SPfloating point products in a slice be given by a[23:0]×b[23:0] anda[55:32]×b[55:32] where bits 23 and 55 are the implied bits in eachoperand. Note that a[23:0]×b[23:0] can be decomposed into computation ofa[22:0]×b[22:0] and computation using the implied bits as illustrated inTable 30 and a[55:32]×b[55:32] can be similarly decomposed. Further, asillustrated in Table 31, a DP floating point product in a slice given bya[52:0]×b[52:0] where bit 52 is the implied bit in each operand can bedecomposed into a computation of a[51:0]×b[51:0] computation using theimplied bits. Note that in each table, the X represents the portion ofthe multiplication attributable to the implied bit and “A” indicatesexponentiation.

TABLE 30 $\quad\begin{matrix}{{{a\left\lbrack {23\text{:}0} \right\rbrack} \times {b\left\lbrack {23\text{:}0} \right\rbrack}} = {\left( {{{a\lbrack 23\rbrack}{{.2}\hat{}23}} + {a\left\lbrack {22\text{:}0} \right\rbrack}} \right) \times \left( {{{b\lbrack 23\rbrack}{{.2}\hat{}23}} + {b\left\lbrack {22\text{:}0} \right\rbrack}} \right)}} \\{= {\left( \left( {{{a\lbrack 23\rbrack}*{b\lbrack 23\rbrack}*{2\hat{}46}} + {{a\left\lbrack {22\text{:}0} \right\rbrack}*{b\lbrack 23\rbrack}*{2\hat{}23}} + {{a\lbrack 23\rbrack}*{b\left\lbrack {22\text{:}0} \right\rbrack}*{2\hat{}23}}} \right) \right) + {{a\left\lbrack {22\text{:}0} \right\rbrack}*{b\left\lbrack {22\text{:}0} \right\rbrack}}}} \\{= {X + {{a\left\lbrack {22\text{:}0} \right\rbrack}*{b\left\lbrack {22\text{:}0} \right\rbrack}}}}\end{matrix}$

TABLE 31 $\quad\begin{matrix}{{{a\left\lbrack {52\text{:}0} \right\rbrack} \times {b\left\lbrack {52\text{:}0} \right\rbrack}} = {\left( {{{a\lbrack 52\rbrack}{{.2}\hat{}52}} + {a\left\lbrack {51\text{:}0} \right\rbrack}} \right) \times \left( {{{b\lbrack 52\rbrack}{{.2}\hat{}52}} + {b\left\lbrack {51\text{:}0} \right\rbrack}} \right)}} \\{= {\left( \left( {{{a\lbrack 52\rbrack}*{b\lbrack 52\rbrack}*{2\hat{}104}} + {{a\left\lbrack {51\text{:}0} \right\rbrack}*{b\lbrack 52\rbrack}*{2\hat{}52}} + {{a\lbrack 52\rbrack}*{b\left\lbrack {51\text{:}0} \right\rbrack}*{2\hat{}52}}} \right) \right) + {{a\left\lbrack {51\text{:}0} \right\rbrack}*{b\left\lbrack {51\text{:}0} \right\rbrack}}}} \\{= {X + {{a\left\lbrack {51\text{:}0} \right\rbrack}*{b\left\lbrack {51\text{:}0} \right\rbrack}}}}\end{matrix}$

The implied bit partial product computation components 3646, 3647, 3648,3649 components are configured to compute “X” using the implied bitvalues from the respective implied bit determination components 3642,3643, 3644, 3645. More specifically, the implied bit partial productcomputation components 3646, 3647, 3648, 3649 compute the partialproduct terms for the implied bit products with the mantissas asillustrated in Table 30 and Table 31. The resulting partial productterms are provided as inputs to the compressor in the respectivemultiply cluster 3604, 3606, 3608, 3610 via one of the multiplexers. Insome examples, the computation of X is implemented using two rows of2-input ‘AND’ gates that output the partial product terms.

For an SP floating point multiply, each adder 3617, 3619 produces arespective final mantissa product and the exponents and signs of thefinal results are produced in parallel by respective normalizationcomponents 3630, 3632. Corresponding exponents, signs, and mantissas arejoined by the respective final result multiplexer 3621, 3623 to generatethe final floating point product as [sign] [exponent] [mantissa] wherethe sign is one bit, the exponent is eight bits, and the mantissa istwenty-three bits. For a DP floating point multiply, the adder 3619produces the final mantissa product and the exponent and sign of thefinal result is produced by the normalization component 3632. Theexponent, sign, and mantissa are joined by the final result multiplexer3623 to generate the final floating point product as [sign] [exponent][mantissa] where the sign is one bit, the exponent is eleven bits, andthe mantissa is fifty-two bits. The final exponent may be incremented ifthere is a carryout from rounding the mantissa product.

In some examples, a multiplication unit in each data path 115, 116includes arithmetic logic for each slice supported by the data path inaddition to the multiplication logic. The arithmetic logic performsarithmetic instructions such as addition, subtraction, minimum, maximum,and Boolean logic operations. FIG. 39 illustrates an exampleconfiguration for a single 64-bit wide slice such as that shown in FIG.36. The M unit slice multiply component 3600 includes arithmetic logic3902 and multiply logic 3904. Operands are routed to the arithmeticlogic 3902 or the multiply logic 3904 based on the decoded instruction.The multiply logic 3904 includes all the multiplication components ofthe M unit slice multiply component 3600 and the multiply logic 3906includes all the multiplication components of the N unit slice multiplycomponent 3602. Arithmetic instructions can be executed by arithmeticlogic 3902 in parallel with execution of single issue multiplyinstructions by multiply logic 3906. In other examples, the arithmeticlogic may be included in the N unit slice multiply logic rather than theM unit slice multiply logic.

FIG. 40 is a flow diagram of a method for performing multiplyinstructions that can be performed by a processor, e.g., processing unitcore 110. In this method, two single issue multiply instructions aredecoded 4000 by the processor. One of the single issue multiplyinstructions is executed 4002 on one multiplication unit of a data pathof the processor, e.g., the scalar data path 115 or vector data path 116of the processing core 110, and the other single issue multiplyinstruction is executed 4004 in parallel on the other multiplicationunit in the data path. While FIG. 40 illustrates the execution of thetwo single issue instructions in sequential boxes, the execution ofthese instructions is performed in parallel. The results of the twosingle issue multiply instructions are then stored 4006 in respectivelocations indicated by the respective instructions.

FIG. 41 is a flow diagram of a method for performing a dual issuemultiply instruction that can be performed by a processor, e.g.,processing unit core 110 (FIG. 1). In this method, a dual issue multiplyinstruction is performed 4100 by the processor to multiply operands ofthe instructions using two multiplication units in a data path of theprocessor, e.g., the scalar data path 115 or vector data path 116 of theprocessing core 110, configured to operate together to determine theproduct of the operands. The product is then stored 4102 in a locationspecified by the dual issue multiply instruction. Dual issue multiplyinstructions are previously described herein.

FIG. 42 is a flow diagram of a method for performing a floating pointmultiply instruction that can be performed by a processor, e.g.,processing unit core 110 (FIG. 1). In this method, a floating pointmultiply instruction is performed 4200 by the processor to multiplyfloating point numbers in which values of implied bits of the leadingbit encoded mantissas of the floating point numbers are determined inparallel with multiplication of the encoded mantissas. The result of thefloating point multiplication instruction is then stored 4202 in alocation indicated by the instruction. Floating point multiplyinstructions are previously described herein.

FIG. 43 is a flow diagram of a method for performing a floating pointmultiply instruction that can be performed by a processor, e.g.,processing unit core 110 (FIG. 1). In this method, a floating pointmultiply instruction is decoded 4300 by an instruction decoder, e.g.,instruction decoder 113 of processing unit core 110. The floating pointmultiply instruction is then executed by a multiplication unit of theprocessor, e.g., multiplication units 223, 224, 243, 244 of processingunit core 110. As part of the execution of the instruction, the valuesof implied bits of the leading bit encoded mantissas of the floatingpoint numbers are determined 4302, multiply operations attributable tothe implied bits are performed 4304 to generate partial product termsfor the implied bits, and multiply operations are performed 4306 withthe leading bit encoded mantissas to generate partial product terms forthe encoded mantissas.

The determination of the implied bit values and the multiply operationsusing these implied bit values are performed in parallel with performingthe multiply operations with the encoded mantissas. While FIG. 43illustrates the determination of the implied bit values, performance ofthe multiply operations using the implied bit values, and performance ofthe multiply operations with the encoded mantissas in sequential boxes,the implied bit processing is performed in parallel the multiplyoperations with the encoded mantissas. The multiplication unit then uses4308 the partial product terms for the implied bits and the partialproduct terms for the encoded mantissas to generate a result of thefloating point multiply instruction.

Referring again to FIG. 1 and FIG. 2, in some examples of processingunit core 110, vector instructions supporting the execution of 8-tap and4-tap finite impulse response (FIR) filters are provided. Such low orderFIR filters are often used in certain application, such as for videoanalytics. For a FIR filter of order N, each value of the outputsequence is a weighted sum of the most recent input values as formallygiven by:

y[n]=b ₀ x[n]+b ₁ x[n−1]+ . . . +b _(N) x[n−N]=Σ_(i=0) ^(N) b _(i)*x[n−i]

where x[n] is the input signal, y[n] is the output signal, N is thefilter order, and b_(i) is the ith coefficient of the filter. Forpurposes of describing the operations performed by the FIR instructionsherein, the follow notation is used:

a[n] or an: data elements in the input stream

c[n] or cn: filter coefficients (also referred to as “taps”)

r[n] or rn: elements in the output stream.

The order of the filter coefficient array c[n] (b[i] in the above formaldefinition) is also redefined to remove the subtraction in thesummation, i.e., the formula used is

r[n]=Σ_(i=0) ^(N-1) c[i]*a[n+i].

The FIR instructions, referred to generically herein as VFIRxxx, varyaccording to the number of filter taps, the size of the input and outputdata elements, and the signs for the operands. More specifically, insome examples, the available instructions are VFIR4HW, VFIR8HW, andVFIR8HD for which both the coefficients and the input data elements aresigned, VFIR4SUHW, VFIR8SUHW, and VFIR8SUHD for which the coefficientsare signed and the input data elements are unsigned, and VFIR4UHW,VFIR8UHW, and VFIR8UHD for which both the coefficients and the inputdata elements are unsigned. In these instructions, the letters HWindicate half-word inputs (16-bits) and word outputs (32-bits) and theletters HD indicate half-word inputs, and double-word outputs (64-bits).

The VFIRxxx instructions are dual issue instructions performed on the M2multiplication unit 243 and the N2 multiplication unit 244, e.g., filtercomputation logic, of processing unit core 110. The instructions arebased on the concept of an instruction taking multiple registers, i.e.,register pairs, for each source and destination. The notation for aregister pair is Register1:Register2, for example VB1:VB0, and the orderof the registers in the pair is high register:low register, e.g.,VB0:VB1 is not allowed. Each instruction has the src1 operand, which maybe referred to as a coefficient operand, as a vector register pairstoring the filter coefficients, the src2 operand, which may be referredto as a data operand, as the streaming engine 125, which provides theinput data elements, and the dst operand as a vector register or vectorregister pair, e.g., VFIRxxx.N2 VB1:VB0, SE0, VB11:VB10. As isillustrated in more detail below, the four or eight filter coefficientsof a VFIR4xx or VFIR8xx operation are duplicated in the src1 operandsuch that a copy of the coefficients is available in each SIMD lane ofthe multiplication units 243, 244 used to perform the specifiedinstruction.

As previously described herein, each multiplication unit 243, 244includes eight slice multiply components, one for each 64-bit slice(64-bit SIMD lane). Further, each slice multiply component includes twomultiply clusters of four 16-bit by 16-bit multipliers. As is explainedin more detail herein, to execute a VFIRxxx instruction on themultiplication units 243, 244, the coefficients cn and data elements asspecified by the instruction operands are mapped to 64-bit SIMD lanescorresponding to the slice multiply components. The ordering of thecoefficients and data elements in each 64-bit SIMD lane indicates howthe coefficients and data elements are mapped to individual multipliersin the corresponding slice multiply components. The mapping varies basedon the particular instruction.

In the detailed descriptions of the VFIRxxx instructions below, Tables32-34 are provided to illustrate this coefficient and data elementmapping for specific instructions. In these tables, the first three rowsshow the inputs per 64-bit slice for the N2 multiplication unit 244 andthe M2 multiplication unit 243 and other rows show the outputs perslice. There are two entries in each cell of the input rows. The topentry indicates the values mapped to one multiply cluster of thecorresponding slice multiply component and the bottom entry indicatesthe values mapped to the other multiply cluster.

FIG. 44 illustrates an example of the operation of the VFIR8HD,VFIR8SUHD, and VFIR8UHD instructions. These instructions take 16-bitinputs (eight coefficients and twenty-three data elements) and producesixteen 64-bit outputs. In this example, the eight 16-bit coefficientsc0-c7 are duplicated in the src1 operand register pair VBM1:VBM0 of theM2/N2/C local register file 233 (FIG. 9). The high register VBM1 storesc7-c4 in each 64-bit SIMD lane and the low register VBM0 stores c3-c0 ineach 64-bit SIMD lane. The twenty-three data elements a0-a22 areprovided by stream 0 (SE0) of the streaming engine 125 (FIG. 28). Thesixteen 64-bit outputs r0-r15 are stored as indicated by the dst operandregister pair in the 64-bit SIMD lanes of global registers VB1 and VB0of the global register file 231 (FIG. 7).

For these instructions, eight rn values are computed on respective slicemultiply components of the N2 multiplication unit 244 and the othereight rn values are computed on respective slice multiply components ofthe M2 multiplication unit 243. Table 32 illustrates which value of rnis computed on each slice multiply component. For example, r0 iscomputed on the slice multiply component for slice 0 in the N2multiplication unit 244 and r8 is computed on the slice multiplycomponent for slice 0 in the M2 multiplication unit 243.

To compute each rn in the multiplication units 243, 244, the dataelements a0-a22 that are provided in sequential order from SE0 arerouted to the appropriate slice multiply component. Table 32 illustrateswhich data elements are routed to each slice multiply component. Forexample, r0=a7*c7+a6*c6+a5*c5+a4*c4+a3*c3+a2*c2+a1*c1+a0*c0 andr1=a8*c7+a7*c6+a6*c5+a5*c4+a4*c3+a3*c2+a2*c1+a1*c0. Accordingly, a0-a7are routed to the slice multiply component of the N2 multiplication unit244 for slice 0 and a1-a8 are routed to the slice multiply component ofthe N2 multiplication unit 244 for slice 1. An approach foraccomplishing this routing is described herein in reference to FIG. 58and FIG. 59.

Table 32 also illustrates which data elements and which coefficients aremultiplied in each multiply cluster in each slice multiply component.For example, c0-c3 and a0-a3 are multiplied in one multiply cluster ofthe slice multiply component of the N2 multiplication unit 244 for slice0 and c4-c7 and a4-a7 are multiplied in the other multiply cluster.Further, c0-c3 and a8-a11 are multiplied in one multiply cluster of theslice multiply component of the M2 multiplication unit 243 for slice 0and c4-c7 and a12-a15 are multiplied in the other multiply cluster.

TABLE 32 Slice 0 Slice 1 Slice 2 Slice 3 Slice 4 Slice 5 Slice 6 Slice 7M2, N2 input  c0-c3  c0-c3  c0-c3  c0-c3  c0-c3  c0-c3  c0-c3  c0-c3 c4-c7  c4-c7  c4-c7  c4-c7  c4-c7  c4-c7  c4-c7  c4-c7 N2 input  a0-a3 a1-a4  a2-a5  a3-a6  a4-a7  a5-a8  a6-a9  a7-a10  a4-a7  a5-a8  a6-a9 a7-a10  a8-a11  a9-a12 a10-a13 a11-a14 M2 input  a8-a11  a9-a12 a10-a13a11-a14 a12-a15 a13-a16 a14-a17 a15-a18 a12-a15 a13-a16 a14-a17 a15-a18a16-a19 a17-a20 a18-a21 a19-a22 N2 output r0 r1 r2 r3 r4 r5 r6 r7 M2output r8 r9 r10 r11 r12 r13 r14 r15

FIG. 45 illustrates an example of which multipliers are used for eachcoefficient/data element multiplication in computing r0 and r8 in theslice multiply components of the M2 multiplication unit 243 and the N2multiplication unit 244 for slice 0. For purposes of this example, the Munit slice multiply component 3600 and the N unit slice multiplycomponent 3602 of FIG. 36 are assumed to be the slice multiplycomponents for slice 0. As indicated in FIG. 45, to compute r0, the fourmultipliers in the multiply cluster 3610 are used in the determinationof a0*c0, a1*c1, a2*c2, and a3*c3, and the four multipliers in themultiply cluster 3608 are used in the determination a4*c4, a5*c5, a6*c6,and a7*c7. Similarly, to compute r8, the four multipliers in themultiply cluster 3606 are used in the determination of a8*c0, a9*c1,a10*c2, and all*c3, and the four multipliers in the multiply cluster3604 are used in the determination of a12*c4, a13*c5, a14*c6, anda15*c7.

FIG. 46 illustrates an example of the operation of the VFIR8HW,VFIR8SUHW, and VFIR8UHW instructions. These instructions take 16-bitinputs (eight coefficients and twenty-three data elements) and producesixteen 32-bit outputs. In this example, the eight 16-bit coefficientsc0-c7 are duplicated in the src1 operand register pair VBM1:VBM0 of theM2/N2/C local register file 233 (FIG. 9). The high register VBM1 storesc7-c4 in each 64-bit SIMD lane and the low register VBM0 stores c3-c0 ineach 64-bit SIMD lane. The twenty-three data elements a0-a22 areprovided by stream 0 (SE0) of the streaming engine 125 (FIG. 28). Thesixteen 32-bit outputs r0-r15 are stored as pairs in the 64-bit SIMDlanes of global register VB0 of the global register file 231 (FIG. 7).

For these instructions, eight rn values are computed on respective slicemultiply components of the N2 multiplication unit 244 and the othereight rn values are computed on respective slice multiply components ofthe M2 multiplication unit 243. The output of the pairs of rn values inthe SIMD lanes of VB0 is performed in the N2 multiplication unit 244.Table 33 illustrates which value of rn is computed on each slicemultiply component. For example, r0 is computed on the slice multiplycomponent for slice 0 in the N2 multiplication unit 244 and r1 iscomputed on the slice multiply component for slice 0 in the M2multiplication unit 243.

To compute each rn in the multiplication units 243, 244, the dataelements a0-a22 that are provided in sequential order from SE0 arerouted to the appropriate slice multiply component. Table 33 illustrateswhich data elements are routed to each slice multiply component. Forexample, r0=a7*c7+a6*c6+a5*c5+a4*c4+a3*c3+a2*c2+a1*c1+a0*c0 andr1=a8*c7+a7*c6+a6*c5+a5*c4+a4*c3+a3*c2+a2*c1+a1*c0. Accordingly, a0-a7are routed to the slice multiply component of the N2 multiplication unit244 for slice 0 and a1-a8 are routed to the slice multiply component ofthe M2 multiplication unit 243 for slice 0. In another example,r4=all*c7=a10*c6+a9*c5+a8*c4+a7*c3+a6*c2+a5*c1+a4*c0 andr5=a12*c7=all*c6+a10*c5+a9*c4+a8*c3+a7*c2+a6*c1+a5*c0. Accordingly,a4-a11 are routed to the slice multiply component of the N2multiplication unit 244 for slice 2 and a8-a11 are routed to the slicemultiply component of the M2 multiplication unit 243 for slice 2. Anapproach for accomplishing this routing is described herein withreference to FIG. 58 and FIG. 59.

Table 33 also illustrates which data elements and which coefficients aremultiplied in each multiply cluster in each slice multiply component.For example, c0-c3 and a0-a3 are multiplied in one multiply cluster ofthe slice multiply component of the N2 multiplication unit 244 for slice0 and c4-c7 and a4-a7 are multiplied in the other multiply cluster.Further, c0-c3 and a1-a4 are multiplied in one multiply cluster of theslice multiply component of the M2 multiplication unit 243 for slice 0and c4-c7 and a5-a8 are multiplied in the other multiply cluster.

TABLE 33 Slice 0 Slice 1 Slice 2 Slice 3 Slice 4 Slice 5 Slice 6 Slice 7M2, N2 c0-c3 c0-c3 c0-c3  c0-c3  c0-c3  c0-c3  c0-c3  c0-c3 c4-c7 c4-c7c4-c7  c4-c7  c4-c7  c4-c7  c4-c7  c4-c7 N2 input a0-a3 a2-a5 a4-a7 a6-a9  a8-a11 a10-a13 a12-a15 a14-a17 a4-a7 a6-a9 a8-a11 a10-a13a12-a15 a14-a17 a16-a19 a18-a21 M2 input a1-a4 a3-a6 a5-a8  a7-a10 a9-a12 a11-a14 a13-a16 a15-a18 a5-a8 a7-a10 a9-a12 a11-a14 a13-a16a15-a18 a17-a20 a19-a22 N2 output r0, r1 r2, r3 r4, r5 r6, r7 r8, r9r10, r11 r12, r13 r14, r15 M2 output — — — — — — — —

FIG. 47 illustrates an example of which multipliers are used for eachcoefficient/data element multiplication in computing r0 and r1 in theslice multiply components of the M2 multiplication unit 243 and the N2multiplication unit 244 for slice 0. For purposes of this example, the Munit slice multiply component 3600 and the N unit slice multiplycomponent 3602 of FIG. 36 are assumed to be the slice multiplycomponents for slice 0. As indicated in FIG. 47, to compute r0, the fourmultipliers in the multiply cluster 3610 are used in the determinationof a0*c0, a1*c1, a2*c2, and a3*c3, and the four multipliers in themultiply cluster 3608 are used in the determination a4*c4, a5*c5, a6*c6,and a7*c7. Similarly, to compute r1, the four multipliers in themultiply cluster 3606 are used in the determination of a1*c0, a2*c1,a3*c2, and a4*c3, and the four multipliers in the multiply cluster 3604are used in the determination of a5*c4, a6*c5, a7*c6, and a8*c7. Forthese instructions, the outputs of the adder 3617 in the M unit slicemultiply component 3600 are routed to the final result mux 3623 in the Nunit slice multiply component 3602 to be merged with the outputs of theadder 3619 to generate the r0, r1 pair output to VB0.

FIG. 48 illustrates an example of the operation of the VFIR4HW,VFIR4SUHW, and VFIR4UHW instructions. These instructions take 16-bitinputs (four coefficients and thirty-five data elements) and producethirty-two 32-bit outputs. In this example, the four 16-bit coefficientsc0-c3 are duplicated in each 64-bit SIMD lane of the src1 operandregister VBM0 of the M2/N2/C local register file 233 (FIG. 9). Thethirty five data elements a0-a34 are provided by the streaming engine125 (FIG. 28) with a0-a31 coming from stream 0 (SE0) and a32-a34 comingfrom stream 1 (SE1). The thirty-two 32-bit outputs are stored as pairsin the 64-bit SIMD lanes of the global register pair VB1:VB0 of theglobal register file 231 (FIG. 7).

For these instructions, sixteen rn values are computed on respectiveslice multiply components of the N2 multiplication unit 244 and theother sixteen rn values are computed on respective slice multiplycomponents of the M2 multiplication unit 243. Table 34 illustrates whichvalues of rn are computed on each slice multiply component. For example,r0 and r1 are computed on the slice multiply component for slice 0 inthe N2 multiplication unit 244 and r16 and r17 are computed on the slicemultiply component for slice 0 in the M2 multiplication unit 243.

To compute each rn in the multiplication units 243, 244, the dataelements a0-a34 that are provided in sequential order from SE0 and SE1are routed to the appropriate slice multiply component. Table 34illustrates which data elements are routed to each slice multiplycomponent. For example, r0=a3*c3+a2*c2+a1*c1+a0*c0 andr1=a4*c3+a3*c2+a2*c1+a1*c0. Accordingly, a0-a3 and a1-a4 are routed tothe slice multiply component of the N2 multiplication unit 244 for slice0. Further, r16=a19*c3+a18*c2+a17*c1+a16*c0 andr17=a12*c3+a19*c2+a18*c1+a17*c0. Accordingly, a16-a19 and a17-a20 arerouted to the slice multiply component of the M2 multiplication unit 243for slice 0. An approach for accomplishing this routing is describedherein in reference to FIG. 58 and FIG. 59.

Table 34 also illustrates which data elements and which coefficients aremultiplied in each multiply cluster in each slice multiply component.For example, c0-c3 and a0-a3 are multiplied in one multiply cluster ofthe slice multiply component of the N2 multiplication unit 244 for slice0 and c0-c3 and a1-a4 are multiplied in the other multiply cluster.Further, c0-c3 and a16-a19 are multiplied in one multiply cluster of theslice multiply component of the M2 multiplication unit 243 for slice 0and c0-c3 and a17-a20 are multiplied in the other multiply cluster.

TABLE 34 Slice 0 Slice 1 Slice 2 Slice 3 Slice 4 Slice 5 Slice 6 Slice 7M2, N2 input  c0-c3  c0-c3  c0-c3  c0-c3  c0-c3  c0-c3  c0-c3  c0-c3 c0-c3  c0-c3  c0-c3  c0-c3  c0-c3  c0-c3  c0-c3  c0-c3 N2 input  a0-a3 a2-a5  a4-a7  a6-a9  a8-a11 a10-a13 a12-a15 a14-a17  a1-a4  a3-a6 a5-a8  a7-a10  a9-a12 a11-a14 a13-a16 a15-a18 M2 input a16-a19 a18-a21a20-a23 a22-a25 a24-a27 a26-a29 a28-a31 a30-a33 a17-a20 a19-a22 a21-a24a23-a26 a25-a28 a27-a30 a29-a32 a31-a34 N2 output r0, r1 r2, r3 r4, r5r6, r7 r8, r9 r10, r11 r12, r13 r14, r15 M2 output r16, r17 r18, r19r20, r21 r22, r23 r24, r25 r26, r27 r28, r29 r30, r31

FIG. 49 illustrates an example of which multipliers are used for eachcoefficient/data element multiplication in computing r0, r1, r16, andr17 in the slice multiply components of the M2 multiplication unit 243and the N2 multiplication unit 244 for slice 0. For purposes of thisexample, the M unit slice multiply component 3600 and the N unit slicemultiply component 3602 of FIG. 36 are assumed to be the slice multiplycomponents for slice 0. As indicated in FIG. 49, to compute r0, the fourmultipliers in the multiply cluster 3610 are used in the determinationof a0*c0, a1*c1, a2*c2, and a3*c3, and to compute r1, the fourmultipliers in the multiply cluster 3608 are used in the determinationa1*c0, a2*c1, a3*c2, and a4*c3. Similarly, to compute r16, the fourmultipliers in the multiply cluster 3606 are used in the determinationof a16*c0, a17*c1, a18*c2, and a19*c3, and to compute r17, the fourmultipliers in the multiply cluster 3604 are used in the determinationof a17*c0, a18*c1, a19*c2, and a20*c3.

FIG. 50 is a flow diagram of a method for performing a vector finiteimpulse response (VFIR) filter instruction that can be performed by aprocessor, e.g., processing unit core 110 (FIG. 1). In this method, avector finite impulse (VFIR) filter instruction is performed 5000 by theprocessor to generate filter outputs using coefficients and sequentialdata elements specified by a coefficient operand and a data operand ofthe VFIR filter instruction. The filter outputs are then stored 5002 ina storage location indicated by the VFIR filter instruction. VFIR filterinstructions are previously described herein.

Referring again to FIG. 1 and FIG. 2, in some examples of processingunit core 110, instructions for vector based multiplication of an 8×4matrix and a 4×8 matrix to generate a 4×4 output matrix are provided. IfA is an m×n matrix and B is an n×p matrix,

${A = \begin{pmatrix}a_{11} & a_{12} & \ldots & a_{1n} \\a_{21} & a_{22} & \ldots & a_{2n} \\\vdots & \vdots & \ddots & \vdots \\a_{m1} & a_{m2} & \ldots & a_{mn}\end{pmatrix}},{B = \begin{pmatrix}b_{11} & b_{12} & \ldots & b_{1p} \\b_{21} & b_{22} & \ldots & b_{2p} \\\vdots & \vdots & \ddots & \vdots \\b_{n\; 1} & b_{n2} & \ldots & b_{np}\end{pmatrix}}$

the matrix product R=AB is the m×p matrix

$R = \begin{pmatrix}r_{11} & r_{12} & \ldots & r_{1p} \\r_{21} & r_{22} & \ldots & r_{2p} \\\vdots & \vdots & \ddots & \vdots \\r_{m\; 1} & r_{m\; 2} & \ldots & r_{mp}\end{pmatrix}$ where$r_{ij} = {{{a_{i1}b_{1j}} + {a_{i2}b_{2j}} + \ldots + {a_{in}b_{nj}}} = {\sum\limits_{k = 1}^{n}{a_{ik}b_{kj}}}}$

for i=1, . . . , m and j=1, . . . , p. That is, an entry r_(ij) of thematrix product R is determined by multiplying term-by-term the elementsof the ith row of A and the jth column of B and adding the n products.For purpose of describing the operations performed by the VMATMPYxxxinstructions herein, the follow notation is used:

a_(ik) or aik: elements of the 8×4 A matrix, where i=0, . . . , 7, k=0,. . . , 3

b_(kj) or bkj: elements of the 4×8 B matrix, where k=0, . . . , 3, j=0,. . . , 7

r_(ij) or rij: elements of the 4×4 matrix R, where i=0, . . . , 3, j=0,. . . , 3.

The matrix multiply instructions, referred to generically herein asVMATMPYxxx, vary according to the size of the output data elements andthe signs of the operands. More specifically, the available instructionsare VMATMPYHW and VMATMPYHD, for which the array elements for both the8×4 array and 4×8 array are signed, VMATMPYSUHW and VMATMPYSUHD, forwhich the array elements in the 8×4 array are signed and the arrayelements in the 4×8 array are unsigned, VMATMPYUSHW and VMATMPYUSHD, forwhich the array elements in the 8×4 array are unsigned and the arrayelements in the 4×8 array are signed, and VMATMPYUHW and VMATMPYUHD, forwhich the array elements for both the 8×4 array and 4×8 array areunsigned. In these instructions, the letters HW indicate half-wordinputs (16-bits) and word outputs (32-bits) and the letters HD indicatehalf-word inputs, and double-word outputs (64-bits).

The VMATMPYxxx instructions can be used to multiply large matrices in8×4 by 4×8 blocks. Such matrix multiplication support is important formultiple applications such as high performance computing (BLAS and BLIStype operations for fixed point), fully connected layers in convolutionneural networks (deep learning), and space applications where precisionof accumulation of results is important. The sizes of the matrices insuch applications are very large and require accumulating themultiplication results as 32-bit or 64-bit without losing precision. Theresults may be rounded and shifted after the matrix multiplication.

The VMATMPYxxx instructions are dual issue instructions performed on theM2 multiplication unit 243 and the N2 multiplication unit 244, e.g.,vector matrix multiplication logic, of processing unit core 110. Foreach instruction, the src1 operand is an 8×4 matrix, i.e., the A matrix,provided by one stream of the streaming engine 125 (FIG. 28), the src2operand is a 4×8 matrix, i.e., the B matrix, provided by the otherstream of the streaming engine 125, and the dst operand is a vectorregister for the instructions producing 32-bit results and a vectorregister pair for the instructions producing 64-bit results, e.g.,VMATMPYHW.N2 SE0, SE1, VB0.

As previously described herein, each multiplication unit 243, 244includes eight slice multiply components, one for each 64-bit slice(64-bit SIMD lane). Further, each slice multiply component includes twomultiply clusters of four 16-bit by 16-bit multipliers. As is explainedin more detail herein, to execute a VMATMPYxxx instruction on themultiplication units 243, 244, the elements of the 8×4 A matrix and the4×8 B matrix are mapped to 64-bit SIMD lanes corresponding to the slicemultiply components. The ordering of the elements in each 64-bit SIMDlane indicates how the elements are mapped to individual multipliers inthe corresponding slice multiply components.

In the detailed descriptions of the VMATMPYxxx instructions below,tables in FIG. 52 and FIG. 55 illustrate the element mapping. In thesetables, the N2 src1 and M2 src1 rows show the mapping of the elements ofthe A matrix per 64-bit slice for the N2 multiplication unit 244 and theM2 multiplication unit 243. The N2 src2 and M2 src2 rows show themapping of the elements of the B matrix per 64-bit slice for the N2multiplication unit 244 and the M2 multiplication unit 243. The bottomrow or rows show the outputs per slice. Each of the input rows includeseight entries per slice. The top four entries indicate the values mappedto one multiply cluster of the corresponding slice multiply componentand the bottom four entries indicate the values mapped to the othermultiply cluster.

FIG. 51 illustrates an example of the operation of the VMATMPYHW,VMATMPYSUHW, VMATMPYUSHW, and VMATMPYUHW instructions. Theseinstructions take 16-bit inputs (thirty-two elements of the A matrix andthirty-two elements of the B matrix) and produce sixteen 32-bit output(sixteen elements of the R matrix). In this example, the thirty-twoelements of the A matrix are provided by stream 0 (SE0) of the streamingengine 125 (FIG. 28) and the thirty-two elements of the B matrix areprovided by stream 1 (SE1) of the streaming engine 125. The sixteen32-bit outputs r00-r33 are stored as pairs in the 64-bit SIMD lanes ofglobal register VB0 of the global register file 231 (FIG. 7).

For these instructions, eight rij values are computed on respectiveslice multiply components of the N2 multiplication unit 244 and theother eight rij values are computed on respective slice multiplycomponents of the M2 multiplication unit 243. The output of the pairs ofrij values in the SIMD lanes of VB0 is performed in the N2multiplication unit 244. The table shown in FIG. 52 illustrates whichvalue of rij is computed on each slice multiply component. For example,r00 is computed on the slice multiply component for slice 0 in the N2multiplication unit 244 and r01 is computed on the slice multiplycomponent for slice 0 in the M2 multiplication unit 243.

To compute each rij in the multiplication units 243, 244, the A matrixelements a00-a37 that are provided in sequential order from SE0 and theB matrix elements b00-b73 that are provided in sequential order from SE1are routed to the appropriate slice multiply component. The table inFIG. 52 illustrates which matrix elements are routed to each slicemultiply component. For example,r00=a00b00+a01b10+a02b20+a03b30+a04b40+a05b50+a06b60+a07b70 andr01=a00b01+a01b11+a02b21+a03b31+a04b41+a05b51+a06b61+a07b71.Accordingly, a00-a07 and b00-b70 are routed to the slice multiplycomponent of the N2 multiplication unit 244 for slice 0 and a00-a07 andb02-b72 are routed to the slice multiply component of the N2multiplication unit 244 for slice 1. An approach for accomplishing thisrouting is described herein in reference to FIG. 58 and FIG. 59.

The table in FIG. 52 also illustrates which matrix elements aremultiplied in each multiply cluster in each slice multiply component.For example, a00-a03 and b00-b30 are multiplied in one multiply clusterof the slice multiply component of the N2 multiplication unit 244 forslice 0 and a04-a07 and b40-b70 are multiplied in the other multiplycluster. Further, a00-a03 and b01-b31 are multiplied in one multiplycluster of the slice multiply component of the M2 multiplication unit243 for slice 0 and a04-a07 and b41-b71 are multiplied in the othermultiply cluster.

FIG. 53 illustrates an example of which multipliers are used for eachmatrix element multiplication in computing r00 and r01 in the slicemultiply components of the M2 multiplication unit 243 and the N2multiplication unit 244 for slice 0. For purposes of this example, the Munit slice multiply component 3600 and the N unit slice multiplycomponent 3602 of FIG. 36 are assumed to be the slice multiplycomponents for slice 0. As indicated in FIG. 53, to compute r00, thefour multipliers in the multiply cluster 3610 are used in thedetermination of a00*b00, a01*b10, a02*b20, and a03*b30, and the fourmultipliers in the multiply cluster 3608 are used in the determinationa04*b40, a05*b50, a06*b60, and a07*b70. Similarly, to compute r01, thefour multipliers in the multiply cluster 3606 are used in thedetermination of a00*b01, a01*b11, a02*b21, and a03*b31, and the fourmultipliers in the multiply cluster 3604 are used in the determinationof a04*b41, a05*b51, a06*b61, and a07*b71. For these instructions, theoutputs of the adder 3617 in the M unit slice multiply component 3600are routed to the final result mux 3623 in the N unit slice multiplycomponent 3602 to be merged with the outputs of the adder 3619 togenerate the r00, r01 pair output to VB0.

FIG. 54 illustrates an example of the operation of the VMATMPYHD,VMATMPYSUHD, VMATMPYUSHD, and VMATMPYUHD instructions. Theseinstructions take 16-bit inputs (thirty-two elements of the A matrix andthirty-two elements of the B matrix) and produce sixteen 64-bit output(sixteen elements of the R matrix). In this example, the thirty-twoelements of the A matrix are provided by stream 0 (SE0) of the streamingengine 125 (FIG. 28) and the thirty-two elements of the B matrix areprovided by stream 1 (SE1) of the streaming engine 125. The sixteen64-bit outputs r00-r33 are stored as indicated by the dst operandregister pair in the 64-bit SIMD lanes of global registers VB1 and VB0of the global register file 231 (FIG. 7).

For these instructions, eight rij values are computed on respectiveslice multiply components of the N2 multiplication unit 244 and theother eight rij values are computed on respective slice multiplycomponents of the M2 multiplication unit 243. The output of each rijvalue into the SIMD lanes of VB0 and VB1 is performed in by themultiplication unit that computes the value. The table in shown in FIG.55 illustrates which value of rij is computed on each slice multiplycomponent. For example, r00 is computed on the slice multiply componentfor slice 0 in the N2 multiplication unit 244 and r01 is computed on theslice multiply component for slice 0 in the M2 multiplication unit 243.

To compute each rij in the multiplication units 243, 244, the A matrixelements a00-a37 that are provided in sequential order from SE0 and theB matrix elements b00-b73 that are provided in sequential order from SE1are routed to the appropriate slice multiply component. The table inFIG. 55 illustrates which matrix elements are routed to each slicemultiply component. For example,r00=a00b00+a01b10+a02b20+a03b30+a04b40+a05b50+a06b60+a07b70 andr01=a00b01+a01b11+a02b21+a4*c4+a03b31+a04b41+a05b51+a06b61+a07b71.Accordingly, a00-a07 and b00-b70 are routed to the slice multiplycomponent of the N2 multiplication unit 244 for slice 0 and a00-a07 andb01-b71 are routed to the slice multiply component of the M2multiplication unit 243 for slice 0. An approach for accomplishing thisrouting is described herein in reference to FIG. 58 and FIG. 59.

The table in FIG. 55 also illustrates which matrix elements aremultiplied in each multiply cluster in each slice multiply component.For example, a00-a03 and b00-b30 are multiplied in one multiply clusterof the slice multiply component of the N2 multiplication unit 244 forslice 0 and a04-a07 and b40-b70 are multiplied in the other multiplycluster. Further, a00-a03 and b01-b31 are multiplied in one multiplycluster of the slice multiply component of the M2 multiplication unit243 for slice 0 and a04-a07 and b41-b71 are multiplied in the othermultiply cluster.

FIG. 56 illustrates an example of which multipliers are used for eachmatrix element multiplication in computing r00 and r01 in the slicemultiply components of the M2 multiplication unit 243 and the N2multiplication unit 244 for slice 0. For purposes of this example, the Munit slice multiply component 3600 and the N unit slice multiplycomponent 3602 of FIG. 36 are assumed to be the slice multiplycomponents for slice 0. As indicated in FIG. 56, to compute r00, thefour multipliers in the multiply cluster 3610 are used in thedetermination of a00*b00, a01*b10, a02*b20, and a03*b30, and the fourmultipliers in the multiply cluster 3608 are used in the determinationa04*b40, a05*b50, a06*b60, and a07*b70. Similarly, to compute r01, thefour multipliers in the multiply cluster 3606 are used in thedetermination of a00*b01, a01*b11, a02*b21, and a03*b31, and the fourmultipliers in the multiply cluster 3604 are used in the determinationof a04*b41, a05*b51, a06*b61, and a07*b71.

FIG. 57 is a flow diagram of a method for performing vector-based matrixmultiplication instruction that can be performed by a processor, e.g.,processing unit core 110 (FIG. 1). In this method, a vector matrixmultiply instruction is performed 5700 by the processor to multiply anm×n A matrix and an n×p B matrix to generate elements of an m×p Rmatrix. The elements of the R matrix are then stored 5702 in a storagelocation indicated by the vector matrix multiply instruction. Vectormatrix multiply instructions are previously described herein.

Referring again to FIG. 1 and FIG. 2, as previously described herein,the streaming engine 125 generally reads data elements from memoryaccording to a stream definition and maps the data elements sequentiallyinto lanes or slices of a vector in increasing lane order. For sometypes of instructions that use vector data from the streaming engine125, the data elements in the vectors generated by the streaming engine125 need to be permuted for correct execution of the instruction on afunctional unit. For example, an instruction for implementing a finiteimpulse response (FIR) filter, VFIR8HD, performs multiplications ofeight 16-bit signed coefficients with eight 16-bit signed samples, andaccumulates the products as 64-bit outputs. The filter coefficients arethe same for all outputs but the samples need to be shifted left bysixteen bits before multiplication in a multiplication unit 243, 244 foreach consecutive output. Similarly, for vector-based matrixmultiplication instructions such as VMATMPYHD, the data from thestreaming engine 125 needs to be arranged such that each row of the Amatrix is multiplied with each column of the B matrix in amultiplication unit 243, 244.

As illustrated in the block diagram of FIG. 58, in some examples, thereis a streaming engine interface 5800 between the streaming engine 125and the vector multiplication units M2 243 and N2 244. The streamingengine interface 5800 includes functionality to buffer data from thestreaming engine 125. The streaming engine interface 5800 also includespermute networks for rearranging or permuting the data elements from thestreaming engine 125 as needed for execution of selected instructions inthe M2 unit 243 and/or the N2 unit 244.

FIG. 59 is a block diagram illustrating an example streaming engineinterface 5800 in more detail. As previously described herein, thestreaming engine 125 (FIG. 28) includes two closely coupled streamingengines, SE0 and SE1, that can manage two data streams simultaneously.The streaming engine interface 5800 includes an SE0 interface component5900 coupled between SE0 and the vector multiplication units 243, 244and an SE1 interface component 5902 coupled between SE0 and the vectormultiplication units 243, 244. A detailed view of the SE0 interfacecomponent 5900 is provided. The SE1 interface component 5902 isidentical.

The SE0 interface component 5900 includes two elastic buffers, EB1 andEB2, coupled to receive 512-bit input from SE0. The elastic buffers areconfigured to store two consecutive packets of data elements from SE0.Each of the elastic buffers EB1 and EB2 is coupled to a respectivepermute component 5904, 5906. Input packets from SE0 are alternated in aping pong fashion between EB1 and EB2. A detailed view of the permutecomponent 5904 coupled to EB1 is provided. The other permute component5906 is identical.

The permute component 5904 includes functionality to arrange (permute)data elements from the elastic buffer EB1 into locations in a 512-bitvector as needed for execution of specific instructions on the M2 unit243 and/or N2 unit 244. The permute component 5904 includes permutenetworks 5908, 5910, 5912, 5914, 5916 configured to permute dataelements for the VFIRxxx and VMATMPYxxx instructions. The 5:1multiplexer 5917 is configured to select the output of one of thepermute networks 5908, 5910, 5912, 5914, 5916 according to theparticular instruction being executed. The select signal (not shown)comes from the instruction decoder 113 (FIG. 1). The multiplexer logic5918 is coupled to the permute component 5904 and the permute component5906 and is configured to select between the outputs of the twocomponents. The select signal (not shown) is set based on which of EB1and EB2 contains the next data packet for the multiplication units 243,244.

The permute network 5908 for the VFIR8×HD instructions permutes thestreaming engine data to the data pattern for performing vectormultiplications for FIR operations for an 8 tap filter as previouslydescribed herein. Table 32 illustrates the permutation results, i.e.,the data pattern, output by the network 5908 for input to the N2multiplication unit 244 and the M2 multiplication unit 243 given thesequential data elements a0-a22. Similarly, the permute network 5910 forthe VFIR8×HW instructions and the permute network 5912 for the VFIR4×HWinstructions permute the streaming engine data to the data patterns forperforming vector multiplications for FIR operations for an 8 tap filteror a 4 tap filter as previously described herein. Table 33 and Table 34respectively illustrate the permutation results of the network 5910 andthe network 5912 for input to the N2 multiplication unit 244 and the M2multiplication unit 243 given the respective sequential data elementsa0-a22 and a0-a34.

The permute network 5914 for the VMATMPY×HW instructions and the permutenetwork 5916 for the VMATMPY×HD instructions permute the streamingengine data for the A matrix and the B matrix to the data patterns forperforming vector multiplications for matrix multiply operations aspreviously described herein. The tables in FIG. 52 and FIG. 55respectively illustrate the permutation results of the network 5914 andthe network 5916 for input to the N2 multiplication unit 244 and the M2multiplication unit 243 given the respective sequential data elementsa00-a37 of the A matrix and b00-b73 of the B matrix.

The OR circuitry 5920 is “glue logic” between the SE interface 5800 andthe vector multiplication units 243, 244. The OR circuitry 5920 isconfigured to select between the output of the SEC) interface component5900 and the SE1 interface component 5902. The OR circuitry is furtherconfigured to concatenate permuted data elements from SE0 and SE1 forthose instructions that receive data elements from both, e.g., theVFIR4xx instructions. The multiplexers 5922, 5924, 5926 coupled betweenthe OR circuitry 5920 and the N2 multiplication unit 244 and the M2multiplication unit 243 are configured to select either SE0 or SE1 asinputs to the respective multiplication unit.

FIG. 60 is a flow diagram of a method for performing permutation ofstreamed data elements that can be performed by a permute network, e.g.,permute networks 5908, 5910, 5912, 5914, 5916 (FIG. 59). In this method,data elements for a vector instruction, e.g., a VFIRxxx or VMATMPYxxxinstruction, are received 6000 in a permute network from a streamingengine, e.g., streaming engine 125 (FIG. 28). The permute network thenmaps 6002 the data elements to vector locations for execution of thevector instruction by a vector functional unit, e.g., multiplicationunits 243, 244 (FIG. 2). Permute networks for vector instructions arepreviously described herein.

FIG. 61 illustrates an example multiprocessor system. In this example,SoC 6100 includes processor 100 (FIG. 1) (referred to as “processor A”)and it is combined with a second processor 6111 (referred to as“processor B”). Each processor is coupled to a block of shared levelthree (L3) memory 6150 via bus 6151. Processor B includes a block ofunshared level two (L2) memory 6112. A direct memory access (DMA) engine6160 may be programmed to transfer blocks of data/instructions from L3memory to L2 memory 130 or L2 memory 6112 using known or later developedDMA techniques. Various types of peripherals 6162 are also coupled tomemory bus 6151, such as wireless and/or wired communicationcontrollers, etc.

In this example, processor A, processor B, L3 memory 6150 are allincluded in a SoC 6100 that may be encapsulated to form a package thatmay be mounted on a substrate such as a printed circuit board (PCB)using known or later developed packaging techniques. For example, SoC6100 may be encapsulated in a ball grid array (BGA) package. In thisexample, external memory interface (EMI) 6152 allows additional externalbulk memory 6154 to be accessed by processor A and/or processor B.

In this example, processor B is an ARM® processor that may be used forscalar processing and control functions. In other examples, varioustypes of known or later developed processors may be combined with DSP100. While two processors are illustrated in this example, in anotherexample, multiple copies of DSP 100 and/or multiple copies of processorB may be included within an SoC.

OTHER EMBODIMENTS

While the disclosure has been described with respect to a limited numberof embodiments, other embodiments can be devised which do not departfrom the scope of the invention as disclosed herein.

For example, embodiments of VFIRxxx instructions are described herein asbeing implemented on a processor in which functional units, data paths,and register files are used for accumulation. In some embodiments, theVFIRxxx instructions are implemented on a processor with an accumulatorbased architecture.

In another example, embodiments of VFIRxxx and VMATMPYxxx instructionsare described herein as being implemented using vector multiplicationunits that designed for vector multiplication instructions. In someembodiments, such instructions are implemented using special purposefunctional units, e.g., filter computation logic and vector matrixmultiplication logic, designed to execute the instructions.

In another example, embodiments of VFIRxxx instructions for 8-tap and4-tap FIR filters are described herein. In some embodiments, VFIRinstructions are provided for other numbers of taps.

In another example, embodiments of VMATMPYxxx instructions formultiplying an 8×4 matrix and a 4×8 matrix are described herein. In someembodiments, VMATMPY instructions are provided for multiplication ofmatrices of other sizes.

In another example, embodiments of VFIRxxx and VMATMPYxxx instructionsare described herein assuming 512-bit vectors and either 16-bit or32-bit or 64-bit elements/lanes. In other examples, the vectors may besmaller or larger and/or the size of the elements may be larger orsmaller.

In another example, permutation of input data elements for VFIRxxx andVMATMPYxxx instructions is described herein using permute networksinside a streaming engine interface. In some embodiments, thepermutations are hardwired in the functional units performing theinstructions.

In another example, permutation of input data elements for instructionexecution is described herein as being implemented with five permutenetworks, one for each instruction type requiring input datapermutation. In some embodiments, more or fewer permute networks areprovided, depending on the number of instructions requiring suchpermutation.

In another example, embodiments are described herein in which a 5-bitconstant stored in the 6-bit SRC2/CST field 1302 (FIG. 13) isconcatenated with 27-bits in a constant extension slot to form a 32-bitconstant. In some embodiments, a 6-bit constant is stored in the 6-bitSRC2/CST field 1302 and is concatenated with 26-bits in a constantextension slot to form a 32-bit constant.

It is therefore contemplated that the appended claims will cover anysuch modifications of the embodiments as fall within the true scope ofthe disclosure.

What is claimed is:
 1. A method comprising: performing, by a processorin response to a vector finite impulse response (VFIR) filterinstruction, generating of a plurality of filter outputs using aplurality of coefficients and a plurality of sequential data elements,the plurality of coefficients specified by a coefficient operand of theVFIR filter instruction and the plurality of sequential data elementsspecified by a data operand of the VFIR filter instruction; and storingthe filter outputs in a storage location specified by the VFIR filterinstruction.
 2. The method of claim 1, wherein a number of coefficientsof the plurality of coefficients is eight.
 3. The method of claim 2,wherein a number of sequential data elements in the plurality ofsequential data elements is twenty-three and a number of filter outputsin the plurality of filter outputs is sixteen.
 4. The method of claim 1,wherein a number of coefficients of the plurality of coefficients isfour.
 5. The method of claim 4, wherein a number of sequential dataelements in the plurality of sequential data elements is thirty-five anda number of filter outputs in the plurality of filter outputs isthirty-two.
 6. The method of claim 1, wherein generating furthercomprises generating the plurality of filter outputs using vectormultiplication units comprised in a vector data path of the processorand configured to perform vector multiply operations, wherein eachvector multiplication unit comprises a slice multiply component for eachslice of the vector data path, and wherein each slice multiply componentgenerates at least one respective filter output of the plurality offilter outputs.
 7. The method of claim 6, wherein the plurality ofcoefficients is duplicated in each lane of the coefficient operand suchthat all coefficients are provided to each slice multiply component, andwherein the method further comprises mapping data elements in theplurality of sequential data elements to each slice multiplicationcomponent based on the at least one filter output to be generated by theslice multiplication component.
 8. The method of claim 7, wherein theplurality of sequential data elements is provided by a streaming engine,and wherein the mapping is performed by a permute network coupledbetween the streaming engine and the vector multiplication components.9. A processor comprising: an instruction decoder configured to decode avector finite impulse response (VFIR) filter instruction; and filtercomputation logic configured to generate, responsive to the VFIR filterinstruction, a plurality of filter outputs using a plurality ofcoefficients and a plurality of sequential data elements, the pluralityof coefficients specified by a coefficient operand of the VFIR filterinstruction and the plurality of sequential data elements specified by adata operand of the VFIR filter instruction.
 10. The processor of claim9, wherein a number of coefficients in the plurality of coefficients iseight.
 11. The processor of claim 10, wherein a number of sequentialdata elements in the plurality of sequential data elements istwenty-three and a number of filter outputs in the plurality of filteroutputs is sixteen.
 12. The processor of claim 9, wherein a number ofcoefficients of the plurality of coefficients is four.
 13. The processorof claim 13, wherein a number of sequential data elements in theplurality of sequential data elements is thirty-five and a number offilter outputs in the plurality of filter outputs is thirty-two.
 14. Theprocessor of claim 9, wherein the filter computation logic comprisesvector multiplication units comprised in a vector data path of theprocessor and configured to perform vector multiply operations, whereineach vector multiplication unit comprises a slice multiply component foreach slice of the vector data path, and wherein each slice multiplycomponent is configured to generate at least one respective filteroutput of the plurality of filter outputs.
 15. The processor of claim14, wherein the plurality of coefficients is duplicated in each lane ofthe coefficient operand such that all coefficients are provided to eachslice multiply component, and wherein data elements in the plurality ofsequential data elements are mapped to each slice multiplicationcomponent based on the at least one filter output to be generated by theslice multiplication component.
 16. The processor of claim 15, whereinthe plurality of sequential data elements is provided by a streamingengine, and wherein mapping of the data elements is performed by apermute network comprised in a streaming engine interface coupledbetween the streaming engine and the vector multiplication units. 17.The processor of claim 9, wherein the processor is a digital signalprocessor (DSP).